The Labor Market

For most of human history, the surplus value of any commodity was ultimately generated exclusively by human labor. Raw materials have no market value on their own: deposits of metal in the earth, oil beneath layers of rock, or timber in the wilderness don’t become commodities until labor is applied, until they are extracted, processed, and given the form in which they can be exchanged. In this sense, a commodity emerges from a sequence of purposeful labor operations that transform the natural state of things into a generally accepted product form.

Raw material provides the material base, but it is labor that adds to it form, functionality, and use value, and with it exchange value. It is on this labor that the markup is applied, which is what forms surplus value. Capital seeks to buy people’s working time as cheaply as possible and sell the results of that time as expensively as possible. Let’s look at all of this in the chart below.

This shows the labor market from the perspective of a single firm. The vertical axis shows wages, the horizontal axis shows the number of workers. There are two main curves. The blue curve is the marginal revenue product of labor, showing how much additional revenue each new employee brings to the firm. It declines because as the number of workers increases, the contribution of each new employee typically diminishes, and the law of diminishing returns kicks in, which I described in some detail in the previous article.

The orange curve represents the marginal cost of hiring. It rises because to attract more workers, the firm has to offer higher wages. And since raising wages may affect not just the new employee but existing ones as well, the actual marginal cost of hiring rises faster.

The intersection of these two curves is the firm’s optimal level of employment. At this point, the additional revenue from a worker equals the additional cost of hiring them. Below this point it’s profitable to hire more; above it, the benefit disappears. The horizontal dashed line through this point shows the equilibrium wage.

The area between the blue curve and the wage level constitutes the employer surplus. This is the difference between what workers bring to the company and what they are paid. The area between the wage level and the labor supply curve (effectively the lower portion of the chart) reflects worker surplus, meaning the difference between the wage actually received and the minimum they would have been willing to accept. That’s the whole explanation of how profit from labor is formed.

Hence the constant corporate logic of seeking out territories where labor costs the least: colonial systems, then the offshoring of production, the relocation of factories and service centers to cheap-labor regions, the exploitation of migrants, and other forms of reducing the cost of human labor. At the root of all this is the widening gap between what an hour of labor costs on the labor market and what a capitalist can sell the results of that hour for on the goods and services market. And that’s precisely why we see the growing pay gap between management and workers.

Of course, the modern financial system has also played a significant role, feeding capital growth for decades with helicopter money, meaning cheap liquidity, credit expansion, and quantitative easing. These mechanisms allowed capital not just to preserve but to widen the gap between labor income and capital income, deepening wealth concentration and the real economy’s dependence on financial flows.

But as I wrote in the previous piece, a new factor has appeared in the modern era: artificial intelligence, which intervenes not just in the level of costs but in the very paradigm of labor pricing. Where added value was once inseparable from human labor, meaning hours of programming, analysis, design, AI is now shifting a significant volume of these operations into automated production, where the marginal cost per unit of output approaches zero after the initial investment in models and infrastructure. Tasks that used to require dozens of man-hours are now completed in milliseconds by a machine, with minimal variable costs and virtually unlimited scalability. This isn’t just the automation of physical labor, as on the assembly line during the Industrial Revolution, but an attempt to reproduce human intelligence, where algorithms replicate and amplify cognitive functions that were previously the exclusive domain of humans.

The result is a product where surplus value is increasingly generated not through the exploitation of human labor but through rent on ownership of technological infrastructure. The hunt for cheap labor gives way to competition for access to models, data centers, and algorithms. The markup shifts from man-hours to machine-time, which doesn’t tire, doesn’t strike, and scales exponentially. The man-hour as the universal metric for valuing labor and cost is distorting, and with it the entire logic of pricing and the distribution of surplus product, forcing a rethinking of the sources of profit in the modern economy.

The Problem Everyone Is Shouting About

AI is already rewriting the nature of programming. Tools like Claude, Cursor, and Copilot generate nearly half of all code, with developer acceptance rates exceeding 30%. According to GitHub, tasks are completed more than one and a half times faster. In practice, AI handles routine work better than a junior specialist with a few years of experience, writes cleaner code, and fixes bugs faster.

Sounds great. But that’s exactly where the problem starts.

Most IT companies built their business model on simple logic: the client pays for specialists’ time. More developers, longer projects, higher fees. If one developer with AI does the work of three, the project requires fewer people and fewer hours.

The client is happy, getting the same result for less. The vendor suffers, because its revenue depends directly on the volume of human labor. That’s the cannibalization dilemma: technology undermining the profitability of the very party deploying it.

The same logic hits the SaaS model from a different angle. A client company would buy 50 Salesforce or Slack licenses at $50-100 per seat, the seat-based model. Growth in headcount automatically meant growth in the monthly bill. A great arrangement, as long as all users were human. Goldman Sachs analysts now warn that the per-user model has no more than five years left. AI agents are taking over, handling tasks end-to-end, from writing reports to closing deals. One agent replaces several employees, and the need for extra seats simply evaporates.

Some companies have already proven this in practice. At Klarna, after deploying agents, revenue per employee doubled and approached nearly a million dollars. SaaStr replaced its sales team with agents and maintained the same revenue. According to BCG, 40% of clients explicitly say they are reducing the number of paid seats to cut costs.

The real paradox is that vendors are trying to retain clients by adding AI to their products, but clients immediately use these features to cut their spending. Take ServiceNow: despite subscription growth and hundreds of millions from new AI services, the stock is falling because investors doubt the scalability of the new model. Profit, meanwhile, is steadily shifting toward owners of infrastructure and computing capacity. The market is responding with lower valuations, with P/E ratios already down from 30 to 20-25.

Vendors face a choice: transition to outcome-based pricing and take a share of the savings they create, or keep losing clients who have learned to count digital agents rather than people.

A note on Salesforce.

Let’s look at a specific case instead of staying in the abstract.

Salesforce is essentially a giant business Excel that stores customer contacts, deals, and reports. Large corporations like Coca-Cola have spent decades loading terabytes of data into it, building integrations, access rights, and processes. Migrating all of this to a custom-built AI product would be enormously expensive and risky. So companies like that will almost certainly stay on the platform as a reliable warehouse of data and relationships.

But the main blow lands on the analytics and AI features. Salesforce sells Einstein Analytics for around $50-75 per user per month. That used to look like a reasonable purchase, since without in-house expertise it was difficult to build dashboards, forecasts, and customer insights. Now you can export the tables to CSV, feed them into any AI service, and get charts, analysis, forecasts, and segmentation in a few minutes, possibly more flexible than anything the built-in module provides. Features that used to command dozens of dollars a month are gradually losing their value, and billions in potential subscription revenue are evaporating with them.

The next blow falls on new customer acquisition. Previously, a startup that needed a CRM almost automatically looked at off-the-shelf SaaS. Now executives realize they can build an MVP with AI assistants in an environment like Cursor or Replit, use an open-source stack, and skip the per-seat rent entirely. For this reason, the market is recording a slowdown in new user additions as clients shift to optimizing existing licenses rather than expanding.

Large platforms with deep data and high migration costs, like Salesforce and Workday, are still holding their ground. But niche SaaS products with a set of analytics widgets are losing value and risk disappearing. The strong get stronger, the weak disappear. AI is simply radically accelerating the natural selection that was always happening anyway.

The Other Side

Since the 2000s, the internet lowered the cost of distributing software. AI is doing the same to the process of building it. Development volume is growing almost exponentially, costs are falling, and as code gets cheaper, companies start automating tasks that used to seem too complex or unpredictable.

But the market sees only one side of the picture and panics. The software company index has fallen by a third since October, driven by the explosion of AI news. Investors have bet on the complete collapse of the entire SaaS model, as if everything will disappear tomorrow. Revenue multiples fell from 18-19 at the 2021 peak to 5.1 by December 2025. Revenue growth slowed from 17% in 2023 to 12.2% in Q4 2025.

Yet operationally, most SaaS companies remain quite healthy. The average public SaaS company generates $179 million in operating cash flow per year. Revenue is growing, free cash exists. But the market is punishing everyone wholesale, without distinction.

That’s the mistake. Market participants are lumping two fundamentally different types of business into one basket and applying the same discount to both.

The first type is deterministic systems: CRM, ERP, accounting. These require absolute precision, rule compliance, integration with client data, and proven business processes. A company that stores client data for decades, executes complex business logic, and integrates with thousands of other systems is critical infrastructure. AI isn’t a competitor here, because errors are unacceptable and migration costs are prohibitive.

The second type is probabilistic systems with pattern recognition, content generation, analytics widgets. If AI can replicate them at 90% of the quality for 10% of the cost, those businesses will collapse. These products are genuinely vulnerable, and the market is right to be worried about them.

But the problem now is that all SaaS trades at the same multiples: those with free cash margins five times over revenue and data on thousands of clients, and those selling one-off reports. That’s the investment opportunity, because once the market notices this difference, multiples will normalize and the opportunity will disappear along with them.

Writing code with AI accounts for roughly 1% of the work. The other 99% is reliability, scaling, security, and updates. Vibe coding is good for drafts, but it doesn’t replace deep systems understanding. Goldman may be right that tectonic shifts are coming in five years. But that’s a forecast, not a verdict.

Adaptation

The pricing model will inevitably move from per-user toward payment for result, volume, or actual value. Charging for AI agents under the old model makes no sense, and vendors will have to accept that whether they want to or not. Two competing paradigms will take its place.

Usage-based: payment for volume, where the number of requests, transactions, computations, and API calls gets monetized. The more actively agents use the platform, the higher the bill. For the vendor this is good news, since agents work around the clock and at scales unavailable to humans, so total consumption volume can grow many times over even as the number of human users shrinks. That’s precisely why some platforms in the new reality won’t get cheaper but more expensive. An agent integrated with ERP and sending thousands of requests per hour generates far more revenue than a manager who opened the CRM three times a day.

The downside of this model is unpredictable costs for the client. Companies used to a fixed software budget suddenly get a bill that depends on how intensively agents are working. This creates a new type of negotiation, where the business must answer not “How many seats do we need?” but “How many operations are we planning per month?” And that’s a question most companies can’t answer right now.

Outcome-based: payment for a specific result, whether a closed deal, a resolved ticket, a processed document, or a generated lead. The model is elegant: the vendor literally takes responsibility for the product’s value, not just for the fact of its delivery. The client pays only when something actually happened. This shifts the relationship from transactional to partnership.

But how do you measure the result? Who’s to blame if the deal didn’t close? Did the agent underperform, or did the manager ruin it on the final call? Outcome-based pricing requires clear attribution, and business processes are rarely that transparent. The first companies to solve this measurement problem will gain an enormous competitive advantage, not because their product is better, but because they’ll be able to prove that it works.

In practice, companies will likely arrive at hybrid models: a fixed platform fee for access to data and infrastructure plus a variable component based on volume or results. Something like a gym membership with a separate fee for a personal trainer, except this trainer never tires and is never late.

Horizontal companies with universal products will remain under maximum pressure, because competition is high and any new player with a smart algorithm can do the same thing faster and cheaper. Vertical companies built around a specific industry, with its rules and processes, are more resilient. To threaten such a business, AI would have to replicate almost the entire industry expertise and offer a price 4-5 times lower at the same time. That’s substantially harder than replacing an analytics widget.

A large auto manufacturer isn’t going to build its own ERP managing billions of dollars. An error for such a company would cost not just a regular employee their career, but top management as well. Nobody applauds a perfect payroll calculation, but a failure gets noticed immediately. It’s simpler to rent from specialists who have worked it out on thousands of clients. Nothing will fundamentally change here, only how the bill is calculated.

In fast-changing areas like technical support or lead generation, older large firms often lose out to speed. But only if AI complements their core, rather than competing with it. New narrow niches will be taken by emerging companies. Large players with data on thousands of clients, complex processes, and convenient APIs for agents will strengthen their position.

Conclusion

Artificial intelligence is not destroying SaaS but rebuilding it for the agent era, where platforms with data and integrations will survive and vulnerable widgets will disappear. The shift to volume or outcome-based pricing will allow machine activity to be monetized, strengthening large players and driving a recovery in the market and multiples accordingly. In the end, profit will shift toward infrastructure, and the labor economy will evolve from the exploitation of man-hours to rent on algorithms, defining the new winners of techno-capitalism (or “techno-feudalism,” to borrow from Yanis Varoufakis).

This isn’t the end of enterprise software, but its upgrade, with a redistribution of who exactly will collect the cream.

And the first to collect it will be those who manage to rewrite not just the product but the price list.

Marginal Returns

In a normal economy there’s a simple rule: the more capital you accumulate, the smaller the return from each additional dollar invested. This is called diminishing marginal returns.

Take a hypothetical company building a mobile app. The law of diminishing marginal returns explains what happens when you keep adding developers to a project with a fixed architecture and codebase.

The increasing returns phase starts with a single developer who writes the backend, builds the interface, handles testing, and talks to the client. When the company hires a second developer, specialization emerges: one focuses on the backend, the other on the frontend. They work in parallel on different modules, code review improves, bugs get caught faster. Productivity grows faster than simple doubling.

When a third developer is hired, say a UI/UX specialist or QA engineer, the team reaches peak synergy. The optimal team size for a mid-complexity project is 3-7 people. A study of 491 projects showed that teams of 3-7 developers demonstrate the best productivity and code quality.

This is also confirmed by the Ringelmann effect. A company can achieve far more with more team members, but individual members of a large team are less productive than their counterparts in smaller ones. According to this theory, the individual effort of each group member declines as group size grows.

The diminishing returns phase begins around the 4th or 5th developer. Features are still being added, but not as fast as before. The architecture is already set, the critical modules are occupied, developers spend more and more time on calls, aligning interfaces, waiting for dependent tasks to complete. The number of communication channels grows by the formula N(N-1)/2. For 5 people that’s 10 channels, but for 8 people it’s already 28. The chart below shows exactly what the exponential growth in communication channels looks like as team size increases.

Each new developer still adds value, but their contribution is smaller than the previous one’s, since more time goes to communication and coordination than to actual code.

Negative returns can set in when the team grows to 10-15 developers without corresponding changes to architecture and processes. The project becomes a coordination nightmare. The point of maximum output is passed and productivity falls, because the team spends more time on synchronization and resolving merge conflicts than on new work.

Each new developer isn’t just useless but actively adds communication overhead for everyone else, slowing the project down. This phenomenon was described by Fred Brooks back in 1975 in “The Mythical Man-Month”:

“Adding manpower to a late software project makes it later.”

This whole construction worked perfectly well until a new factor appeared: AI. It’s not just equipment that helps workers; it can itself perform some tasks that previously only people could do. If you add AI capital into this chain, you simultaneously add “effective labor.”

But how is AI different from the first machines and steam engines? Those also sharply increased labor productivity. Maybe this is just a new industrialization? That comparison doesn’t quite hold.

The first wave of industrialization (steam engines, looms, assembly lines, etc.) did make individual workers far more productive. One person with a machine produced as much as five without one. In the terminology, this is also growth in “effective labor,” since output per worker grows. But there’s one important detail: the number of people barely changed, while capital kept increasing. The “capital per worker” ratio grew, and the marginal return from each new machine eventually began to fall. The first machine produces a huge gain, but the hundredth will make a barely noticeable difference. And we’re back to classical diminishing returns.

With AI the logic is different: it represents a special type of capital that behaves not merely as a labor amplifier but as labor itself. This means that each additional piece of AI capital adds not just capacity but new virtual workers. You didn’t just buy a server; you bought a server plus ten digital employees who themselves read, think, write, and review. As more such AI agents are added, both capital and the effective number of workers grow.

Effective Labor

The Goldman Sachs chart showing how exposed different sectors of the economy are to AI automation, plotted against their labor costs as a share of revenue, shows exactly where the risk of human labor displacement is concentrated.

Now imagine that a company bought not just a GPU cluster but a cluster plus thousands of virtual programmers, data analysts, and copywriters who work 24/7. As a result, the ratio of IT capital to effective labor stays roughly constant, and the return on infrastructure doesn’t fall, it holds at a stable level. There’s an entire range of compute expansion where marginal returns stay flat rather than declining. That’s the first key point.

Now add the second mechanism: wealth distribution in the IT industry. AI shifts income from engineers and programmers to technology company owners. Why? Because AI replaces some high-paid specialists and the share of wages in revenue falls. The shareholders’ share of profit grows in turn.

When wealth concentrates among venture capitalists and founders of tech giants, their savings rate increases. A Silicon Valley billionaire doesn’t spend all their capital on consumption; they reinvest an enormous share back into startups and infrastructure. This is confirmed in the joint research by Piketty and Zucman based on data for wealthy countries, showing that the ratio of total wealth to income grew over recent decades (from 200-300% in 1970 to 400-600% by 2010).

Another article by Zucman on increasing global wealth inequality shows that the share of wealth belonging to the top 1% of the population has grown noticeably since the 1980s, while the share held by the bottom 75% has remained low. Wealth is becoming more concentrated, especially among those who already hold substantial financial resources, meaning the potential sources of large-scale ecosystem investment.

This means that in the IT ecosystem, the aggregate supply of capital available for investment is growing, and the required return on that capital falls. In other words, as tech capitalists get richer, they’re willing to fund projects at lower rates of return, simply because they have more money to reinvest into the ecosystem. This is called the “capital feedback cycle,” where capital works as a closed cycle. That’s why we see exponential growth in CapEx.

Three Equilibria

Now let’s combine these two effects. On one side, we have a flat section of IT capital returns thanks to AI. On the other, as infrastructure grows and tech billionaires get richer, the required return on that capital falls. What this means mathematically is that instead of a single stable state (where capital growth leads to falling productivity), the IT ecosystem can have three stable states: three equilibria.

Think of the IT industry as a system that can stabilize at three different levels of development. These levels are defined by two variables:

K is capital, meaning the volume of IT infrastructure (data centers, GPU clusters, trained models). K_ai is the threshold for the start of AI deployment, and K_sat is the AI saturation threshold.

q is Tobin’s q, showing how expensively the market values that infrastructure relative to its actual replacement cost. That is, if a company had to build all its data centers, buildings, servers, networks, etc. from scratch today, it would cost roughly $n billion. That’s the replacement cost of capital. For example, if a company’s market value is $15 billion and its replacement cost is $10 billion, then q = 1.5.

Each equilibrium is defined by points on the chart.

The first equilibrium, called “low” (K approximately 0.226), sits to the left of the K_AI threshold, before mass AI deployment. At this stage AI exists but is used in a limited way, mainly for experimental projects rather than as a mass tool for replacing human labor. Returns on capital are high because infrastructure is scarce. But the required return for investors is also high, which means wealth is limited, the consumption rate is high, and investors demand a large return on their investments. These two forces balance each other out. Tobin’s q sits at a fair level of around 1.02 and the market values companies at their real asset value.

This state resembles the IT industry in early 2022, before the ChatGPT revolution. Data centers are operating, cloud services are running, but AI hasn’t yet become a mass-market tool. Companies invest cautiously because they don’t see revolutionary opportunities for rapid growth. The ecosystem gets stuck at this point because nobody believes a transition to a higher state is possible, so there’s no incentive for massive investment.

The second equilibrium, called “middle” (K approximately 0.379), sits between the K_ai and K_sat thresholds, meaning inside the AI deployment zone. This is a theoretical point where infrastructure is at a medium level, AI is partially deployed, returns on capital are on a plateau thanks to the labor substitution effect, and required returns are at a medium level. Mathematically this equilibrium exists, but it’s fundamentally unstable, because in the AI deployment zone (Region II) the return on capital (r^K) is flat, since it doesn’t fall as infrastructure grows.

This means there’s no natural brake on growth. The return that investors require keeps declining as their wealth grows, which creates a positive feedback loop in both directions.

If capital grows a little, wealth increases, required returns fall (investors become less demanding and are willing to accept smaller yields), investing becomes more profitable since meeting lower hurdles is easier. So capital grows even faster and the system moves toward high equilibrium.

If the reverse happens and capital falls a little, wealth decreases, required returns rise, investing becomes unprofitable, capital falls further, and the system slides back toward low equilibrium.

The IT ecosystem with the AI factor won’t linger at the second equilibrium. It will either break through it in a surge of growth on the way to high equilibrium, or roll back to the low one (along the red line). This is a little like balancing on a tightrope: you can be there for a moment but not indefinitely. In practice, the industry can cross this point while riding a wave of optimism following an AI breakthrough and keep moving forward, as long as confidence doesn’t crack.

The third equilibrium, called “high” (K approximately 0.792), sits to the right of the K_sat threshold, meaning after full AI saturation. Imagine that the industry is saturated with IT infrastructure: data centers are built at scale, GPU clusters are deployed everywhere, LLMs are trained to the limits of current technology, and all tasks that can be automated with today’s AI already have been. AI capital has reached its peak investment and is no longer growing, either because of technological constraints (models have hit a quality ceiling), or because training data is exhausted, or because of organizational and regulatory barriers. Returns on capital are again low because there’s an abundance of infrastructure and the classical laws of diminishing returns apply. But the required return for investors is even lower, since the ecosystem’s wealth is enormous and tech billionaires are willing to invest at minimal rates because there are very few alternatives for deploying capital. These forces again balance each other out, but at a new, higher level. Tobin’s q returns to a fair level of around 1.02, the same as in the low equilibrium.

The key paradox is that Tobin’s q in the high equilibrium is the same as in the low one (around 1.02). But the total capitalization of the industry (q × K) is 3.5 times larger, real infrastructure is enormous, ecosystem wealth is colossal, and productivity is an order of magnitude higher. The IT ecosystem is stable and wealthy. This is the hypothetical IT industry of 2030, if the transition succeeds. Every company uses AI agents for routine tasks, all data centers are packed with specialized chips, LLMs are embedded everywhere, from enterprise software to household devices. Further growth is evolutionary, without revolutions, through improvements to existing systems. Company valuations return to reasonable multiples, but against a massive asset base. The industry waits for the next qualitative breakthrough, whether true AGI, or a fundamentally new architecture based on quantum computing.

Where Are We Now?

It’s quite probable that we’re currently in the critical transition phase, specifically between K_ai and K_sat. To start with, Claude Code (from Anthropic), which writes software code, currently accounts for 4% of all public commits on GitHub. This is February 2026. Analyst forecasts suggest this figure will grow to twenty percent by the end of this year. One in every five commits in the global code repository will be written not by a human but by artificial intelligence. Ryan Dahl, creator of NodeJS, says outright that “writing syntax by hand is no longer an engineer’s job.”

According to a Stack Overflow survey, eighty-four percent of developers already use AI at work. However, only thirty-one percent have moved to full coding agents. But this means the penetration curve is only just beginning and we’re witnessing the mass deployment of a technology that replaces highly-paid skilled labor.

Companies report that one developer with Claude Code now does in a week what used to take a whole team a month. A human costs $350-500 per day in fully-loaded costs, while an AI agent costs $6-7. Return on investment of ten to thirty times. With that math, you can’t stop the adoption. Now let’s return to the Caballero model and its key thresholds.

The first threshold (K_ai) is the moment when enough IT infrastructure has accumulated for mass AI deployment that begins to replace human labor. Before this threshold, AI exists but is used in a limited way. After it comes Region II, where returns on capital hold at a plateau because each new GPU cluster adds not only computing power but also virtual workers. Effective labor grows together with capital, the capital-labor ratio stays constant, and marginal returns don’t fall.

The second threshold (K_sat) is the saturation point, when AI has reached the limit of deployment under current technologies. Beyond this, growth comes only from regular infrastructure, the magic ends, and returns begin to fall again under classical laws.

We probably already crossed the first threshold K_ai around 2024. ChatGPT launched at the end of 2022, generated an explosion of interest in 2023-2024, and 2025 became the year of mass adoption.

Now, in February 2026, we see Claude Code capturing 4% of commits and moving toward 20%. This means we’re inside Region II (the AI deployment zone), where returns are flat and rapid growth is possible without the classical constraints of diminishing returns. AI is partially displacing human labor, companies are scaling infrastructure at breakneck speed, returns are holding at a high level, and growth is limited only by the availability of computing power. Anthropic is adding more monthly revenue than OpenAI. The market is still far from saturation.

But we definitely haven’t reached the second threshold K_sat yet, since growth is not slowing but accelerating. Only 31% of developers use agents, meaning an enormous reserve for penetration growth remains untouched. The horizon of autonomous tasks that AI can perform without human involvement is doubling every four to seven months. Currently an agent can work autonomously for about five hours, but that’s far from multi-day projects like a full audit or comprehensive system refactoring.

Moreover, coding is only a beachhead, not the final destination. Ahead lie enormous numbers of knowledge workers worldwide: analysts, lawyers, financial professionals, consultants, marketers. Anyone who reads information, applies knowledge, and produces structured output is in the line of fire. Accenture has signed a contract to train 30,000 employees to work with Claude, focusing on financial services, healthcare, and the public sector. These are enormous untouched markets.

So we’re somewhere between K_ai and K_sat, at the very center of Region II, roughly a third of the way from the deployment threshold to the saturation threshold.

In that case, where does Tobin’s q sit? Again, q shows how expensively the market values companies relative to the actual replacement cost of their infrastructure. In a normal state, q is around 1.02 (fair company valuation). During speculative growth q jumps higher, and these inflated valuations are what stimulate capital expenditures. It’s profitable for companies to issue expensive shares and build data centers. But in the Caballero model the positive and negative scenario trajectories show abstract dynamics, not a calendar-calibrated path for the real economy, in order to simplify understanding of the model. This means the green and red trajectories are simply examples of two possible scenarios.

Real values differ based on the actual valuations of companies today. Currently q is at or near its peak, possibly in the range of 1.25-1.3. Many tech companies are trading at inflated multiples, Anthropic and OpenAI are raising billions at relatively modest revenue, and euphoria surrounds AI companies. These are the classic signs of inflated valuations.

But most importantly, these inflated valuations are not an irrational bubble. They’re entirely rational. A high q (or high valuation) is the fuel of the technological transition. Without inflated valuations there’s no investment boom, without a boom there’s no rapid infrastructure accumulation, without infrastructure you can’t reach high equilibrium. In other words, to travel from low K (capital, infrastructure volume) to high K, you need a period when q is significantly above normal. This isn’t an error but a necessary part of the mechanism.

Anthropic and OpenAI are scaling compute aggressively precisely because the market gives them capital at high valuations. This capital turns into real data centers, GPU clusters, and trained models. Speculative optimism is materializing into physical infrastructure, not just inflating useless assets.

What Could Cause a Collapse?

Right now K (capital) is roughly at the 0.379 point, q around 1.25-1.3. This is the most dangerous moment in the entire trajectory. From here, if investor confidence holds, q (the high valuation) should start declining while K continues growing toward the saturation threshold along the green trajectory and onward to high equilibrium. But if that confidence breaks, a crash begins and everything follows the “Crash path” scenario.

But what exactly could break the trajectory? There are several scenarios, and some could be happening simultaneously.

First: a premature technological ceiling. The scaling laws that predict model quality improvement with increases in size and data volume suddenly stop working. Models hit a ceiling, the autonomous task horizon stops expanding. Investors realize that further progress is impossible without a qualitative breakthrough on the level of AGI, and there isn’t one. Panic sets in.

Second: regulatory shock. Governments introduce strict restrictions on replacing humans with AI, transparency requirements kill commercial models, and bans in critical industries sharply narrow the market.

Third: economic crisis. A recession forces companies to cut capital expenditures, the GPU boom stops, investments freeze. In my view, a fairly likely scenario.

Fourth: competitive shock. Don’t dismiss the scenario where Microsoft loses ground to startups, OpenAI loses its lead to Anthropic, or Chinese models like DeepSeek crush Western company valuations by demonstrating that AI has become a commodity without meaningful product differentiation.

Fifth: public backlash. Mass layoffs of programmers generate political pressure, “AI took our jobs” narratives gain momentum, and public opinion turns against tech giants. Unlikely, but worth noting.

Microsoft, for instance, is already caught in a kind of trap between two businesses. On one side, Azure (their cloud platform), which rents GPU capacity to companies like OpenAI and Anthropic. That’s the future growth, the terminal value of the company. On the other, Office365, which is what generates enormous profit. But to earn revenue in Azure, Microsoft has to rent its infrastructure to competitors who are destroying its own products. Claude for Excel does what Copilot for Excel was supposed to do, but it was launched by an outside party.

To protect the Office suite, Microsoft has to invest heavily in AI, but those very investments are accelerating the destruction of the per-user-seat software model. Microsoft CEO Satya Nadella has effectively taken on the role of AI product chief, stepping back from day-to-day CEO duties, so high are the stakes. If Microsoft stumbles, it could trigger a chain reaction of investor panic. It would be fatal if the largest market participant loses to startups.

Meanwhile OpenAI is also under threat. Anthropic is outpacing them in monthly revenue growth, and Claude Code is outpacing GitHub Copilot and the ChatGPT API in adoption rates. OpenAI risks becoming just a provider of compute tokens rather than a company building full-stack solutions and AI agents. If the market leader loses, investors begin to doubt the durability of the entire ecosystem.

Any of these triggers, or a combination of them, could cause q to drop sharply. AI company stock prices collapse over days or weeks. q falls from 1.3 to, say, 1.0 or even 0.95. The industry slips off the green trajectory and lands on the red one, back toward low equilibrium.

Then capital expenditures freeze. Companies cancel GPU orders, halt data center construction. Startups don’t get funded and go bankrupt. Infrastructure stops growing and existing infrastructure begins to degrade (depreciation consuming capital faster than new capital is added).

Suppose that in 2027-2028 capital starts falling, after which the industry slowly rolls back to the left on the chart, losing all the progress of recent years. A few years later, around 2030, everything returns to the starting point: K around 0.22, low equilibrium, with q back at its fair level of 1.02. All AI infrastructure is written off as non-functional, the industry has returned to a pre-ChatGPT state. This is a self-fulfilling crash, where pessimism reduces valuations, which strangles investment, which makes the future worse.

The Alternative

In this case it’s the preservation of confidence, without serious triggers. If the coordination of expectations between investors, companies, and consumers holds long enough, the trajectory continues.

2026-2028: Claude Code becomes the standard, capturing 20%+ of commits. Accenture trains tens of thousands, financial institutions, consulting firms, and law firms begin automation. The autonomous task horizon keeps growing, from five hours to twelve hours to multi-day projects. Capital grows from 0.4 to 0.6. q starts declining from its peak of 1.3 toward 1.2, because capital accumulation and wealth growth lower the required investor return.

In this hypothetical scenario, around 2028-2029 the industry crosses the K_sat threshold. This will be the inflection point when all the obvious applications of current LLMs are exhausted. Coding is fully automated. Customer support, data entry, and basic analytics are automated. Capital invested in AI reaches its maximum and freezes. Further infrastructure growth continues only through regular servers (no new virtual employees are added and the effective total workforce stabilizes). The magic of Region II ends, and returns on capital begin to fall again under classical laws. But by this point the industry is already far ahead.

q continues declining from 1.2 toward 1.1, because inflated valuations are no longer needed, accumulated infrastructure is enormous, and required returns have fallen to historical lows.

By 2029-2030+, capital (K) slowly grows from 0.64 toward 0.79. Growth slows because returns are falling. Investment continues, but it’s no longer a boom, just steady accumulation. The wages of remaining workers (those AI didn’t replace) will start growing because capital deepens and productivity rises, though a significant share of the population may remain unemployed. Labor’s share of revenue stabilizes at a new, lower level. q continues falling from 1.1 toward 1.02. By end of 2030 the industry reaches capital of around 0.79, q returns to fair value, and high equilibrium is achieved.

In this scenario IT infrastructure will be enormous. AI is fully integrated into all processes, every company uses LLM agents for routine work. Programmers, analysts, and designers work at the level of architectural decisions and strategy, with details delegated to AI. The share of wages in tech company revenue is lower, but productivity is an order of magnitude higher. Company valuations return to reasonable multiples, but total industry capitalization will be colossal because the asset base has grown many times over. The industry waits for the next qualitative breakthrough, whether true AGI, quantum computers, or a fundamentally new architecture. That scenario sounds like a fairy tale, nothing less.

Conclusion

Don’t forget: Region II, where we currently are, is the most dangerous moment of the entire transition. The next year or two could decide everything. Either we’ll pass the peak and begin the descent toward high equilibrium, materializing speculative optimism into a real economic transformation. Or confidence will break, valuations will collapse, and we’ll roll back down, writing off the AI boom as yet another bubble.

The Caballero model shows that both of these outcomes are rational, both are possible, and the choice between them depends not on fundamental factors that can be measured today, but on the collective belief of market participants that the transition will complete successfully. This is a coordination game where everyone wins if everyone keeps believing, and everyone loses if someone doubts first. That’s why all of this is considered rational but fragile.

Hold on tighter to the bar: the wind is picking up.

The goal of this article is to look at the situation not from the perspective of someone reading cartoon headlines and memes, but from the perspective of someone willing to examine what’s happening with a cold head.

A Brief Primer on Deficits

Every so often, alarming news surfaces about the U.S. national debt hitting its ceiling, the budget falling apart, states unable to pay their bills, and so on. All of it sounds dramatic, but let’s be honest: this isn’t just exaggeration, it’s a myth packaged in a scary headline. It repeats year after year, decade after decade.

Imagine taking out a loan. But there’s a catch: you print the currency for that loan yourself. Literally. You just fire up a home printing press and pull out money whenever a payment is due. Sounds like fraud? Maybe. But if you’re the U.S. government, it’s called monetary policy.

Your “bank” (the rest of the world) doesn’t just accept this money, it eagerly accumulates it. Because these aren’t just dollars, they’re the global reserve currency, used in international trade, savings, and even currency interventions. The entire rest of the world extends credit to the U.S. in dollars that the U.S. prints itself.

This arrangement is the product of a post-war geopolitical construction. After World War II, the U.S. found itself in a unique position: the largest economy left undamaged by the war, with the most powerful industrial base, enormous gold reserves, and critically, a dollar pegged to gold. That was the Bretton Woods system, where the dollar became the anchor of the global financial architecture.

Then in 1971, President Nixon closed the “gold window.” The dollar, and with it every other currency, permanently lost its peg to gold. From that point on, paper money existed purely by government decree, not through any backing, creating an era of financial flexibility where constraints became political rather than material. The dollar’s gold peg under Bretton Woods was, in essence, a conditional formality. Yes, the dollar was officially convertible to gold, but in practice money was already being issued in volumes far exceeding the gold backing. Gold was a symbolic pillar of confidence, not an actual brake on the printing press. So the abandonment of the gold standard in 1971 didn’t change the underlying reality: it merely removed the need to pretend that reserves placed any limits on money supply. The last risk of the U.S. running out of gold disappeared, and along with it went the external constraints on financial expansion.

Over time, the U.S. became not only the issuer of the world’s primary currency but also its primary consumer. Its manufacturing base partially relocated to Asia (mainly China), while America imported ever more goods, paying for them with its own dollars. This gave rise to the growth of the U.S. fiscal and trade deficits.

These deficits are not a malfunction of the system. They are its foundation. To sustain economic growth, employment, and consumption, the government spends more than it collects in taxes: a fiscal deficit.

That spending goes toward infrastructure, healthcare, defense budgets, and social programs, maintaining domestic demand. At the same time, the American economy runs on mass consumption: people buy a lot and often, but a large share of those goods is produced abroad. This creates a trade deficit, where the U.S. imports more than it exports.

To finance both deficits, the U.S. issues debt: Treasury bonds, bought by both private investors and foreign governments. Why? Because those investors end up holding dollars from selling goods to the U.S., and those dollars need somewhere to go. If China sells a massive shipment of iPhones to the U.S. and receives dollars in return, what do you do with those dollars? That’s why U.S. government debt remains the most liquid and reliable asset.

The result is a kind of closed loop:

Both deficits, fiscal and trade, grow in sync, feeding each other. More spending means more imports. More imports mean more offshore dollars recycled through bond purchases, creating room for new spending. The more America consumes, the more credit it receives. The more credit it receives, the more it can consume. At present, consumption accounts for 70% of U.S. GDP.

The U.S. fiscal and trade deficits, together with the growth of public debt, form the foundation of the current global economic order. They facilitate the accumulation of financial assets domestically, support the consumption and welfare of American citizens, finance allies (including in defense), and feed the global financial system by generating demand for dollars and American assets worldwide. This is partly why the U.S. accounts for roughly 67% of global equity market capitalization.

So if the carousel has been spinning this long, why worry?

Debt Risks

Let’s look at this from several angles, not just one.

First: Warren Buffett’s view on the U.S. deficit spiral. Back in 2003, Warren wrote an interesting letter that captured the real problem of mounting debt and deficits through a single parable about two fictional island nations: Squanderville and Thriftville (a reference to the “Triffin dilemma”).

In the parable, the residents of Squanderville lived lavishly (consuming a lot, producing little). They were accustomed to a high standard of comfort, but covered their needs through imports from the neighboring island of Thriftville, where people lived modestly, worked hard, and saved. Squanderville couldn’t pay for imports with its own goods: it barely had any. Instead, it paid with IOUs and sold off portions of its assets (land, businesses, bonds). Thriftville happily accepted these assets, since they generated income.

Over time, Thriftville’s residents became owners of an ever-larger share of Squanderville. Their ownership stake grew. They began to hold buildings, factories, banks, even land. Squanderville still lived well, but no longer on its own means: it was selling its future. In that future, Thriftville’s thrift turned into economic dominance, while Squanderville’s extravagance turned into a loss of control over its own country.

Through this metaphorical parable, Buffett argues that the current generation in the U.S. benefits from overconsumption, but future generations will be forced to pay for it through interest on debts and dividends to foreign asset owners, while also suffering the consequences of currency debasement. It sounds strange, given that the U.S. still dominates globally, backed by its growing economy. But look at the actual ownership data Buffett points to:

Foreign ownership of American assets and liabilities has exceeded 100% of U.S. GDP. Looking specifically at equities, the share of U.S. companies owned by foreign investors has risen sharply since 1945 and reached 18%.

This indicates that as fiscal and trade deficits grow, so does the volume of foreign capital in the American economy, and that share will keep rising as public debt and interest payments increase. Buffett’s Squanderville parable is not empty noise.

Second: Niall Ferguson’s “Ferguson’s Law.” This is the moment when interest payments on government debt exceed defense spending. That’s where the gradual erosion of a globally dominant superpower’s status begins. Geopolitical power, however you frame it, costs money. If the lion’s share of the budget goes not toward projecting strength but toward servicing debt, it becomes clear the leading state lacks the capacity to maintain the same world order that underwrote its dominance.

This produces a double blow. On one side, the state starts cutting its own defense: armies wear down, research programs get shelved, and allies hear only vague expressions of solidarity instead of concrete support. On the other, global partners, long accustomed to “Uncle Sam” pulling out a checkbook and an aircraft carrier at any moment, begin to wonder whether he can actually fund the security banquet anymore. Trust is a currency, not a natural endowment. It leaves the moment a sign of financial weakness appears on the horizon.

History shows that such turning points are not coincidences but patterns. From late Rome to the British Empire, the story is the same: when a state starts spending more than it can afford, it gradually loses the ability to fund its military, support its allies, and shape events beyond its borders. It becomes increasingly occupied with servicing old debt rather than building the future. America, for the first time in nearly a century, has arrived at the edge of this vortex. The question is not whether it knows what it’s doing. The question is whether it recognizes the language its own numbers are already speaking. And the numbers are striking:

Interest payments on the national debt have already surpassed spending on defense and Medicare, which Trump is so fond of cutting.

No surprise, then, that the U.S. has so abruptly reversed course on its foreign and domestic policy. When you take off the rose-colored glasses and look at the real numbers, it becomes obvious what’s at stake: U.S. global dominance and the entire world order, including the dollar system. And whatever you think of the policy shift, you can call the Trump team a bunch of fools and Trump himself a clown: it doesn’t matter. Because like it or not, the U.S. faces a systemic problem, and it will not disappear by magic.

You could, of course, argue that Trump single-handedly broke everything. But let’s be honest: did one person run the U.S. for all these decades? This is not a system failure. This is the system. The political, economic, and financial landscape of the U.S. was built over decades on the assumption that you can borrow endlessly, print endlessly, import endlessly, and still maintain global dominance. At some point the bills come due. Current decisions and changes are not the cause: they are the symptom.

That’s why the situation resembles a terminal diagnosis, where the patient has exactly two options. Either begin treatment, however painful, grueling, and uncertain, with a chance of recovery. Or let things run their course and get a sudden but entirely predictable death at some future date. And treatment at this point can no longer be outpatient: emergency surgery is needed.

There’s one very convenient argument claiming that U.S. government debt has already reached similar levels before, back in the 1940s, and nothing catastrophic happened. 120% of GDP? Seen worse:

But the problem is that in 1945, the U.S. had not only more steel and factories than the rest of the world combined, but also a comfortable luxury: no bombs on its own territory.

For one thing, America in the 1940s was not a finance-and-services empire built on consumption. It was an industrial monster, armed to the teeth with factories, ships, and motivation. Europe and Asia lay in ruins, and the U.S. exported literally everything, from tanks to toothpaste. Today, core supply chains sit in China, Vietnam, and anywhere but Detroit.

For another, the debt of the 1940s didn’t just grow on its own: it was the price of a world war that mobilized the entire economy. People worked, saved, bought war bonds out of patriotism rather than yield. Most importantly, after the war they returned to an economy primed for a manufacturing boom, not Twitter influencing and a deficit-ridden federal budget.

Today, debt grows because that’s how the system was built, because deficits became a way of life and deindustrialization became the norm. In the 1940s, the Fed tried to cap rates and coordinate with Treasury. Today, everyone more or less pretends everything is under control.

And social obligations should not be forgotten. If in the 1940s wages were low, consumption limited, and health insurance sounded like a utopia, today’s taxpayer expects pensions, hospitals, and preferably no tax increases. Yes, the U.S. climbed out of its debt peak back then, largely through GDP growth, exports, a demographic boom, and a monopoly on producing essential goods with cheap labor. Today, debt grows without a war, without mobilization, without a plan, and without brakes.

Comparing the current debt situation to 1945 is like comparing an ultramarathoner who ran the full distance in body armor and boots to someone who just sits around getting fat on a calorie credit card.

Geopolitical Risks

While some comfort themselves with World War II debt analogies, here is one scenario that would genuinely make things worse: war. Not one war, but several at once, and not necessarily with direct American military involvement. It’s enough for allies to be fighting while the U.S., as the coalition’s senior partner, delivers democracy in crates of ammunition. According to the Global Conflict Tracker, 28 armed conflicts are currently burning across the planet.

Say the war in Ukraine continues, escalation breaks out on the Korean Peninsula, Arab states launch coordinated action against Israel, China invades Taiwan, and the U.S. decides to strike Iran to halt its nuclear program. If China backs Iran, that means a direct collision of interests between the world’s two leading powers, with “volunteer” Chinese formations on Iranian soil.

All of this would require weapons deliveries, allied support, expanded presence, resource mobilization, and enormous sums of money that don’t exist. The source would be increased public debt and currency emission. That would push interest rates up and drive debt servicing costs far higher, deepening the deficit spiral. Not a favorable game in the long run.

That said, allies remain critically important for preserving the global order and maintaining the status of leader. This applies especially to Europe, where many countries have spent years failing to meet NATO’s 2% of GDP defense spending target, counting on U.S. protection. Under current conditions, with spending rising, that model is no longer viable. And even if European countries sharply raised defense budgets above the 2% threshold today, it needs to be clearly understood that you cannot make up for years of underfunding overnight. This is not just a budget number: it’s decades of lost time.

Looking at all of this soberly, it becomes clear that these are exactly the factors shaping Trump’s harsh and often unpopular decisions. His actions are not irrational: they reflect an attempt to adapt to new circumstances in which the U.S. can no longer afford to bankroll half the world. In that context, questions of sovereignty or human costs in the developing world may become secondary. The primary priority is America’s own interests: not scattering resources on secondary tasks that might not support but actually undermine the viability of the global financial system and the country’s own defense capacity.

At face value, comparing U.S. and Chinese military budgets seems obvious. America spends close to a trillion dollars a year, while China spends roughly a third of that. But absolute numbers don’t always reflect real capability.

The American military budget is less about strike power than about the infrastructure of global presence. Hundreds of military bases worldwide, vast logistics, pension obligations, veteran support programs, expensive research, and private contractor agreements: all of it generates sprawling expenditures. The U.S. still has the most powerful military in the world, but its resources are stretched thin, and its effectiveness in local conflicts is increasingly questioned (the Afghanistan withdrawal being the most recent example).

China, by contrast, is focused on projecting force within its own region. Its strategy does not aim for global military dominance but for control over its immediate neighborhood, primarily Taiwan and the South China Sea. Beijing invests with precision: hypersonic missiles, next-generation air defense systems, cyber forces, satellite technologies. It simply avoids unnecessary costs, while maintaining a powerful industrial base that gives it autonomous weapons production.

The core difference lies in approaches to military economics. In the U.S., the private sector plays an enormous role: Lockheed Martin, Raytheon, and other defense contractors set the pace of innovation, but operate under market incentives where profit consistently outranks necessity. China operates through a centralized system with fast decision-making, state-controlled spending, no tender disputes and no political compromises.

Against these two models, the European Union looks like a structure where everyone acknowledges the importance of security but few want to pay for it out of their own pocket. Member states have armies and budgets, but collective combat readiness is diluted. Diversity of standards, absence of unified command, and political fragmentation leave Europe dependent on NATO. Germany spent years unable to reach its 2% GDP defense target, France maintains capability but avoids acting without allies, and Eastern European countries, despite ramping up military efforts, are still not positioned to play a decisive role in collective defense.

Russia presents an entirely different case. Its military budget is modest by global standards, but the spending structure is aimed at specific objectives: maintaining influence in neighboring countries, projecting force in select zones of interest, and nuclear deterrence. The priority is not expensive technological development but large-scale production of artillery, tanks, drones, and ammunition. The war in Ukraine has exposed weaknesses in both logistics and tactics, while simultaneously demonstrating a high degree of combat readiness. Possession of a full weapons production stack, including nuclear, remains a significant factor that constrains the range of external pressure.

Against this backdrop, a number of autocratic allies with their own regional ambitions are beginning to shift the balance of power in global politics, coordinating not through summits and press releases but through direct, back-channel, fast-moving agreements, without parliamentary pauses and long approval chains. Such claims carry enormous weight in the context of the current financial architecture, built on America’s persistent fiscal and trade deficits. But if at some point confidence in that model begins to crack, the blow will fall not only on the U.S. but on the entire system it sustains. Market democracies are vulnerable not only to tanks but to volatility. Any serious weakening of the dollar as a reserve currency, or loss of control over global capital flows, will raise the cost of debt, collapse budgets, and ultimately reduce defense spending, meaning a reduced ability to protect both the country’s own interests and those of its allies.

In this sense, the attack can come not through guns but through bonds (stopping purchases of U.S. debt or outright dumping it), not through invasions but through trade rerouting (alternative markets and other currencies). And if in the past the main instrument of power was an army, today the main instrument is increasingly trust, or its absence. China doesn’t need the yuan to become a reserve currency. It only needs to undermine confidence in the current one, the dollar. And that can be accomplished simply by accelerating the deficit spiral through more military conflicts and market expansion.

As conflicts multiply, U.S. spending will rise, while tax revenue will struggle to keep up, widening the fiscal deficit. Meanwhile the U.S. is trying to maintain influence in economic zones worldwide while pushing back against China. Just look at how global trade dominance has shifted between the two. In the early 2000s, the U.S. held leading positions in international trade, dominating economic zones with high external trade volumes.

Today the picture has reversed entirely. China has become one of the world’s leading trade partners, aggressively expanding its export capacity and deepening its role in global supply chains. This has driven a redistribution of economic and trade influence toward China.

That means new American agreements with other countries, loans, subsidies, and investments in infrastructure and security across unstable regions will require large expenditures with no guaranteed return. The effectiveness of the American defense apparatus is also in question, given that too many contracts run through private corporations where profit interest routinely outranks strategic value. So it’s not just the volume of spending that grows: it’s the sense of overextension. And when other countries begin to see that debt is growing, interest on it is rising, conflict zones are expanding, and economic returns are shrinking, confidence in the dollar and American assets starts to erode. Investors demand higher yields, which raises the cost of debt, demand for U.S. Treasuries contracts, and the dollar loses its “safe haven” status.

Conflicts and economic expansion, then, are not simply creating risk: they are becoming catalysts that amplify the vulnerability of the American financial model. China can simply watch and give the process a gentle nudge.

In that scenario, U.S. inaction is just waiting for the existing order to collapse, while action is a painful attempt to preserve dominance, or the final throes before an inevitable collapse that could drag on for years. The only question is how effective the stabilization tools will be.

Which brings us to the central question: what are tariffs actually for?

Trade Wars

No one can describe the tariff war plan better than the head of the Council of Economic Advisers in the Trump administration. Back in 2024, he worked as chief strategist at Hudson Bay Capital, where he wrote a paper titled “A User’s Guide to Restructuring the Global Trading System,” laying out all the economic levers that would be used to shift America’s economic and geopolitical course:

“If the U.S. is no longer willing to bear the burdens of the current order, it will take steps to change it.”

Tariffs raise the cost of imports, making American goods relatively cheaper. In theory, this should stimulate domestic production, bring back jobs, and reduce the trade deficit. But if foreign currencies weaken in response, the dollar strengthens, imports don’t get more expensive, and American exports become even less competitive. So weakening foreign currencies against the dollar acts as a compensation mechanism that makes American goods more attractive on foreign markets.

But under the administration’s plan, tariffs won’t be uniform. Trump adviser Scott Bessent proposes dividing countries into “good,” “bad,” and “neutral” based on criteria like currency manipulation, compliance with NATO commitments, or support for China at the UN. Friends of the U.S. (those who pay for NATO and don’t manipulate their currency) get low tariffs, while adversaries get crushed under the weight of high ones. The idea is to make access to the American market a privilege, not a right, while forcing allies to share the security burden.

This isn’t only about protecting industry: it’s also about money. In 2018-2019, tariffs generated billions that were effectively paid by China through yuan depreciation. Why? By weakening the yuan, China absorbed the tariff cost increases and kept its goods cheap and accessible, leaving consumer prices manageable. China essentially reduced its manufacturers’ yuan-denominated revenues.

Now, with the U.S. fiscal deficit at 7% of GDP and the Social Security trust fund projected to be exhausted by 2033, tariffs are seen as a potential source of funding for tax cuts or new factories.

Of course, tariffs fit perfectly into national security rhetoric. Trump once said, “If you don’t have steel, you don’t have a country.” In that logic, tariffs can help protect critical industries like steel, semiconductors, and pharmaceuticals from Chinese espionage and dependence on unreliable supply chains. If China were to cut off chip or drug supplies tomorrow, the U.S. would find itself in a vulnerable position.

Tariffs are meant to create a powerful incentive to relocate production to the U.S. Semiconductors are the brain of the modern economy, from smartphones to missiles. Dependence on Taiwan and China (where 60% of global chip production is concentrated) is a key American vulnerability. Tariffs on Chinese chips will make them uncompetitive, pushing companies like TSMC or Intel to build plants in Arizona or Texas. The CHIPS Act of 2022 already allocated $52 billion for this, but tariffs add a lever of pressure.

Pharmaceuticals are another raw nerve. The U.S. imports 80% of active pharmaceutical ingredients from China and India. In a crisis (think COVID) this can become catastrophic. Tariffs on Chinese drugs and components will push companies like Pfizer toward relocating production. But relocating complex manufacturing takes years, requires billions in investment, and demands skilled workers that the U.S. doesn’t have in abundance. Plus, China can keep retaliating with its own tariffs, complicating exports of American technology. This is a chess match where every move carries losses: geopolitical zugzwang.

But that’s only one part of Miran’s plan. The second part is weakening the dollar. In the administration’s view, an overvalued dollar is the root of all problems, and it needed to be weakened. The problem is that the dollar, as the global reserve currency, is persistently overvalued, with an exchange rate higher than what balanced trade would require. Dollar overvaluation is driven not only by domestic consumption policy and deficits, but above all by its status as the world’s reserve currency, which creates persistent demand for the dollar regardless of the trade balance. A strong dollar, in turn, sustains the consumption model and the growth of deficits (and public debt accordingly).

Weakening the dollar is the only option for increasing competitiveness, shrinking the trade deficit, and reviving industry, while simultaneously pressing geopolitical rivals. Miran sees two paths to this.

Option One: Mar-a-Lago Accord

Historically, the U.S. weakened the dollar through international agreements like the 1985 Plaza Accord, when allies agreed to let their currencies strengthen (yen, deutschmark), so the dollar could fall and American exports could recover. The current administration is apparently planning its own version, called the “Mar-a-Lago Accord.” Its central goal: persuade Europe, China, and Japan to strengthen their currencies so the dollar weakens and the U.S. regains its price advantage.

The problem is that no one wants to play this game. Europe is sitting on negligible growth and is comfortable with the status quo. Japan is still wary of the deflation that hit it in the 1990s. China is holding onto exports. Diplomacy won’t work here: you need carrots and sticks. The stick is tariffs. The carrot is tariff removal. And to amplify the pressure, the next idea emerges: century bonds.

The idea is that countries under the U.S. “security umbrella” (NATO, Asian allies) must buy not just short-term bonds but hundred-year bonds. In other words, America says plainly: “Want our protection? Pay for the next one hundred years.” This simultaneously:

1. Locks in external demand for government debt, relieving pressure on the American budget (a 7% GDP deficit is a problem).

2. Forces allies to sell dollars and buy their own currencies to invest in long-dated paper: the dollar weakens.

3. Links defense and economics: want tariff relief, buy our debt and fund NATO.

Selling this idea to China is impossible, of course. China will sooner park reserves in gold (which it’s been doing for a while) than sign up for a century-long attachment to U.S. debt. Europe won’t be enthusiastic either, given economic stagnation. But through the dollar, tariffs, and Treasuries, the U.S. wants to change the rules of the game in its favor.

Option Two: IEEPA and Currency Reserves

Miran noted that the Wall Street view that exchange rates depend solely on Fed rate decisions is wrong, since the Trump administration can take many steps independent of the central bank’s decisions (which, frankly, even surprised me).

If diplomacy fails, Trump will turn to a more radical tool: the International Emergency Economic Powers Act (IEEPA). This economic Swiss Army knife allows for sanctions, asset freezes, transaction blocks, and a “tax” on foreign dollar reserves.

The idea is to impose a “parking fee” on foreign central banks’ holdings of U.S. Treasuries. For example, withholding 1-2% annually for the privilege of accessing dollar liquidity.

This would make dollar reserves less attractive. Countries, to avoid these costs, would start reducing dollar assets and thereby strengthen their own currencies. The dollar would weaken, and the U.S. trade balance would improve. But if you overdo it, you can break the whole structure: the dollar goes into steep decline, yields on bonds soar, and the U.S. mortgage market could collapse again. So it needs to be done gradually.

There’s one more option: the Treasury could begin manipulating currency exchange rates directly. By selling dollars and buying euros, yen, or even yuan, it would artificially raise foreign currency rates, weakening the dollar through direct intervention. The catch is that it costs money. To accumulate $1 trillion in reserves, you’d need to either pull those dollars from the market or print them. And printing means inflation risk, which the Fed is fighting so hard to contain.

Trade as a Weapon

The Trump administration wants to tie trade to national security, turning the U.S. into a fortress surrounded by tariff walls and loyal allies. The idea is to make the world share the burden of providing reserve assets and the security umbrella. If you don’t pay for NATO, don’t fight China, or steal American intellectual property: welcome to the high-tariff club.

“Trade terms can be used as a tool to achieve better outcomes in security and burden-sharing. A clearer delineation of the international economy into security zones with shared principles and economic systems would help identify persistent imbalances and introduce more friction points to address them. Countries that want to be under the U.S. security umbrella should also be under the umbrella of fair trade.” — from Miran’s document.

China is the main adversary. Enormous tariffs are an attempt to strangle China’s export model and force it to open markets, stop technology theft, and strengthen the yuan. If other countries join the “tariff wall” (say, the EU imposes heavy duties on Chinese cars), China ends up isolated. But if they refuse, the U.S. wins anyway, collecting revenue from their exports.

There’s also an alternative scenario where China could avoid tariffs by investing in the U.S. Imagining Chinese car factories in America is difficult, but it would be a repeat of the Reagan-era playbook, when Japan built factories in the states to avoid tariffs. The problem is that China, obsessed with control, is unlikely to agree to export its factories and create jobs for Americans. Reality diverges from theory further still: trading partners don’t sit still.

So what we’re actually watching today differs from the script above. Trump didn’t start with a gradual tariff increase. He went in with a shock, stunning absolutely everyone without exception. The effective tariff rate has returned to early 20th century levels, which means one thing: we’ve entered territory unfamiliar not just to us but to several generations before us.

This is a blow to allies’ exports, with their currencies beginning to strengthen (trade surpluses falling), as tariffs restrict the flow of dollars abroad, reducing demand for dollar assets and for the dollar itself. Sarcastically speaking, that’s already a win: the dollar weakened without any negotiations. The American administration made a sharp first move in its attempt to reshape the system through leverage. But there’s a complication.

Foreign investors (presumably China) started dumping Treasuries, pushing yields up and increasing the risk of even greater debt loads and fiscal deficits with a weakening dollar. China may have needed only a small intervention for algo traders to pick it up in a low-liquidity market with few buyers.

Rising yields mean new bond issuances come at even higher rates, while old ones get replaced upon maturity. We’re back where we started: rising yields mean rising debt service costs. This is an enormous risk that will gradually erode leadership. Recession fears triggered a flight to alternatives: bonds from other countries and gold. That further weakened the dollar and strengthened foreign currencies as demand shifted to alternative safe havens. As a result, Trump had to step back somewhat, announcing a 90-day pause on tariffs for all trading partners except China, to slow the avalanche of negative consequences from an abrupt tariff policy.

And so a kind of negotiation is unfolding, one capable of pushing the global economy toward a situation where recession would look like a walk in the park. This is literally shock therapy for the global economy, and it will have a sequel. Worth quoting one of the best macroeconomists of our time, Olivier Blanchard:

The central problem is this: if such aggressive tariff policy continues for all four years of Trump’s presidency, and trading partners keep retaliating in kind, destabilization is inevitable. Businesses and ordinary citizens under uncertainty will try to minimize risk: holding back investment, building up rainy-day cash, and cutting spending, which is already happening. The result is a sharp contraction in economic activity that could trigger something worse than a recession, possibly a new depression. The economist Thomas Sowell is well-suited to this context:

One can argue that mass unemployment in the 1930s was caused less by the 1929 stock market crash, as is commonly assumed, than by the Smoot-Hawley Act, which sharply raised tariffs on imports. Although unemployment did begin rising after the market collapse, it didn’t exceed 10% in the twelve months that followed. But just five months after Smoot-Hawley was signed, unemployment shot to 11.6%, then continued rising past 20%, staying above 11.6% for eight years. This suggests that protectionist policy triggered the collapse of global trade, deepening the economic downturn.

During the Depression, protectionist measures also undermined international cooperation, intensified economic conflicts, and made countries more isolationist and aggressive, creating ideal conditions for the rise of dictatorships and radical ideologies, and ultimately World War II and the restructuring of the global order.

Industrial Fizzle

Equally important is Americans’ misreading of Trump’s “manufacturing revival” through tariffs, where everyone imagines job growth. In the modern world, this sounds more like a political slogan than an actual strategy. Even in China, the world’s factory for so long, manufacturing employment is not growing: it’s declining.

This is not because China is losing trade wars. It’s because robots, algorithms, and automation systems are simply displacing people from the production cycle. In the U.S. today, only about 8% of employed workers are in manufacturing, and even among them, half are not factory workers in the classical sense but office workers, accountants, logistics specialists, and other support roles.

If the Trump administration continues betting on extremely high tariffs as a way to stimulate production, the result could be severely negative. Global supply chains will fracture, raw materials will get more expensive, exports will lose competitiveness, and automation will keep reducing labor demand, raising unemployment not just in manufacturing but across all related sectors.

As for global supply chains: WTO projections already forecast that Chinese goods exports to the U.S. will collapse by 77% in 2025.

Trump’s tariff pressure and political rhetoric are making the American market unattractive and toxic. But China is not panicking. As the data shows, it is simply planning to redirect its flows to wherever it can trade: Latin America, other parts of Asia, even Africa.

Meanwhile, other countries also dependent on the American demand model are feeling the effects of tariff policy. Their imports into the U.S. are starting to stall as well. The winners are those supplying goods the American economy cannot do without (resources, energy, raw materials), along with countries willing to fill vacated niches with cheap labor and flexible terms with minimal demands.

For the U.S. itself, all of this means higher prices, supply disruptions, and further complications of already tangled logistics chains. Breaking dependence on China sounds like a beautiful idea in theory and an expensive indulgence in reality. Especially when that dependence extends beyond cheap T-shirts and electronics to raw materials: the very foundation on which green energy, advanced technology, and defense are built, all the things the U.S. wants to hold global leadership over. When a deficit can no longer serve as a source of strength, the only remaining universal lever is control over what is genuinely valuable: resources and technology, where the latter depends on the former. But there’s a problem there too: China near-monopolizes the rare earth supply chain and the processing of critical resources.

And as the U.S. severs ties with Beijing, it urgently needs to figure out where to source what’s being cut off. That’s why talk of joint American-Russian Arctic projects carries strategic weight. Greenland’s resources are also back in focus, despite its inaccessibility.

Meanwhile, against the backdrop of war, sanctions, and frozen relations, Americans are increasingly pushing a mineral resources deal with Ukraine, and also with Russia, aimed at driving a wedge into the solid Russia-China alliance and trying to build a more independent resource base. When resources and the preservation of dominance are on the table, ideology about democratic values takes a back seat.

But replacing China and restarting supply chains takes years and careful diplomacy. And while the tariff wars continue, business lives in a state of uncertainty, navigating a minefield where any decision could prove fatal. In that instability, the silhouette of a new global recession is visible, not an industrial resurrection.

An economic downturn amid aggressive tariff policy will only worsen the situation. To cushion the blow and support consumer demand, the government will again have to cut rates, increase spending, and issue new debt obligations. That in turn will raise the cost of debt servicing and perpetuate the chronic fiscal deficit. Under those conditions, breaking out of the deficit spiral becomes not just difficult but practically impossible. With each new round of stimulus, the system only digs itself deeper into debt dependence. Accordingly, the weakening of the dollar architecture and the loss of global leadership sounds not like a hypothesis but like a scenario slowly unfolding.

Conclusion

Protectionism? It hits the consumer and allies. Continuing deficits? It deepens the debt spiral. Weakening the dollar? It undermines confidence in the reserve currency. Raising rates? It suffocates the economy. Cutting them? It accelerates inflation and weakens the dollar further. This is geo-economic zugzwang, where every next move worsens the position and could prove destructive, while inaction only postpones the inevitable.

When Trump launched the trade war with China, it initially looked like just another populist move: “America is losing,” “we’re being exploited,” “we need to bring back factories.” But behind these loud slogans stood a fairly well-structured plan. The idea was to redistribute global supply chains, reduce the trade deficit, and restore some degree of American independence from Asian manufacturers. The Hudson Bay Capital document lays out these goals in detail: regain control, restore sovereignty, rebuild the architecture of global trade.

The strategy is clear. It exists.

But that’s where the rational part of its execution ends. Instead of using diplomatic channels or working toward long-term agreements, Trump chose the path of abrupt action: tweets, sudden tariff changes, public intimidation of allies. What should have been a strategic reassessment of global trade through tariffs has turned into an unpredictable show.

So even when the decision rests on logic, that guarantees neither competent execution nor safe consequences. The global economy is cracking severely, not so much because of shifts in underlying structures or institutions, but because one man decided it was better to slash first and ask questions later. Whether that turns out to be better in the long run remains an open question.

The task facing the administration is, frankly, one that may be unsolvable. But that doesn't mean the approach has to be equally chaotic. If anything, the attempt to solve it should be surgically precise, not a merciless bloodletting whose consequences can only accelerate the collapse of the current world order while continuing to widen the deficit. Living with a deficit is possible for a while. But the outcome is predetermined.

Samuel Benner’s Life Before the Crisis

Samuel Benner was born in 1832 into a farming family in Ohio. From childhood he was accustomed to hard work on the land and grew up surrounded by agricultural traditions. The economic instability of the mid-19th century posed serious problems for farmers dependent on crop prices. Nevertheless, before the crisis of 1873, Benner managed to run a relatively successful farm.

In the 1870s, the U.S. economy was booming, driven by railroad construction and industrial development. Farmers were actively expanding their operations, often taking on debt. Then the growth period came to an abrupt end when the collapse of several large railroad companies triggered a large-scale financial panic.

The Crisis of 1873

The 1873 crisis happened because many people and companies had borrowed too much and invested in projects that weren’t profitable. In the U.S. and Europe, railroads were hit especially hard, having been everyone’s great hope. When they began going bankrupt, many people lost their money and banks closed. This led to a major economic collapse: people lost jobs and businesses shut down. All of it happened because of too much trust in risky investments and poor use of capital.

This event was a heavy blow for many farmers, including Samuel Benner. The main problems he faced included:

1. A sharp drop in agricultural commodity prices: due to falling demand and the general economic downturn, prices for goods produced by farmers fell to critically low levels. This led to enormous losses and made it impossible to service debts.

2. A heavy debt burden: many farmers, including Benner, had taken out loans to expand their operations during the economic boom. When prices fell, servicing those loans became impossible, leading to widespread bankruptcies.

3. The absence of tools for predicting crises: at the time, it was difficult for farmers and entrepreneurs to anticipate economic downturns. Any market shifts happened suddenly, which made them especially destructive.

The core problem Benner was trying to solve was the lack of tools for farmers and entrepreneurs to forecast market fluctuations. At the time, prices for agricultural commodities and industrial assets could shift sharply under the influence of external factors: weather conditions, political crises, and market speculation.

Benner’s Cycles offered a simple but effective forecasting method that allowed:

Farmers to plan production and sales more effectively, maximizing profit. Investors to make better-informed decisions about buying and selling assets, avoiding panic selling and investing during periods of low prices. Entrepreneurs to plan business development with an eye to future economic downturns and upturns.

The Emergence of Benner’s Cycles

After going bankrupt, Benner began collecting and analyzing historical price data on various commodities, including iron, corn, cotton, and pork. The idea for the cycles came to him after long reflection on why economic downturns arrive suddenly and with frightening regularity. He noticed that looking at price history over an extended period revealed certain recurring patterns. In 1875, Samuel Benner published a book titled “Benner’s Prophecies of Future Ups and Downs in Prices.” It laid out the findings of his research and presented forecasts for the future. The book included charts and tables illustrating price cycles for various commodities, along with recommendations on when to buy and sell assets.

The key solutions Benner proposed were as follows:

1. Planning sales and purchases: farmers and entrepreneurs were advised to sell products at the peak of price cycles and purchase resources during price downturns.

2. Avoiding panic selling: knowing about the recurrence of panic cycles, investors could stay calm during downturns and avoid selling assets at depressed prices.

3. Long-term planning: using the predictable cycles, one could plan business development years in advance, avoiding excessive debt loads during economic downturns.

Samuel literally studied every year, every price movement, and found that behind the apparent chaos lay a distinct rhythm. Pig-iron price cycles followed a specific pattern: the periods between price lows shrank in sequence, running 11, 9, and 7 years before a new cycle began. Meanwhile, the periods between price highs lengthened, running 8, 9, and 10 years. In both cases, full cycles consisting of three such periods spanned 27 years. The rhythm of rises and falls was also orderly. Rises lasted 4, 3, and 2 years; declines lasted 7, 6, and 5 years.

Benner connected these cycles to the influence of historical events, wars and financial crises, showing that external factors contribute to the natural course of economic cycles. In his view, major panics also occur with their own specific periodicity:

“Cycles of panics and fluctuations in prices of farm and industrial products are but the effects of a cause, which manifests in periods of 16, 18, and 20 years for panics. These cycles repeat in the same order every 54 years, with periods of 8, 9, and 10 years for metal prices, which also return in the same order every 27 years.”

One of the most well-known predictions Benner made during his lifetime was his forecast of the next major economic crisis, which did arrive in 1893, twenty years after the Panic of 1873. His theory of 20-year panic cycles was confirmed, which drew additional attention to his work from economists and investors.

“Benner’s chart so accurately predicted the pig-iron price cycle that through World War II the profit-to-loss ratio was 44 to 1.” — Edward Dewey, Chief Economic Analyst of the U.S. Department of Commerce and Director of the Foundation for the Study of Cycles, 1967.

The minor cycles on the commodities Benner studied align with the larger crisis cycles, which would later be organized into three variations of peak (A, B) and low (C) asset and commodity prices:

A: Years in which crises and panics began or will begin, which may repeat in the future.

B: Years of good times with high prices, when selling stocks and other assets is recommended.

C: Years of hard times with low prices, when buying assets and waiting for price growth is recommended.

Simplified, each cycle takes the following form:

Although after World War II pig-iron prices stopped correlating with Benner’s cyclicality, the cyclicality of economic crises remained.

The constant development of our world cannot be overlooked. In the 19th century, the economy was focused on industrial development, where pig iron was the key resource for building railroad infrastructure, advancing shipping, and driving heavy industry, while coal served as the primary energy source, enabling large-scale production. In the 20th century, the center of gravity shifted to energy and mass production. Oil became the defining resource, driving transportation, electricity, and the chemical industry. The introduction of assembly lines, pioneered by Ford, revolutionized production and made goods more accessible. Human labor was gradually replaced by mechanization and automation.

From the 1970s onward, the digital revolution took hold. Microprocessors, the internet, and personal computers transformed the economy, placing intellectual labor at the center, with data becoming the new key resource. Biotechnology opened new possibilities in medicine and agriculture.

We are now entering an entirely new era. The economy is no longer about production: it’s about the creation of knowledge. Artificial intelligence, quantum computing, renewable energy, biotechnology: these are the new engines of progress. But it’s not just technology that’s changing. The entire social context is shifting. Education, finance, law: all of it is adapting to the new reality. We’re moving from an age of “making with hands” to an age of “thinking and creating.”

The Past

A small discussion on Reddit, where someone posed a perfectly logical question: “Does this predict, for example, the Spanish silver crash of the late 1500s?”, became the impulse for exploring a broader time frame. To get an answer, the data had to be extrapolated backward by 500 years.

The answer to the original question about the Spanish silver crash of the late 1500s is 1575. Under the reign of Philip II, silver played a key role in Spain’s financial position. In the early 1540s, Spain began receiving enormous flows of silver from the New World, which became an important source of revenue for the crown. But silver revenues proved unstable, creating financial risks. In the lead-up to the 1575 crisis, there was a substantial drop in silver inflows, which aggravated the crown’s financial difficulties. When Philip II faced the need to finance military campaigns and silver revenues fell short of expectations, the king could not service his short-term debts. This led him to suspend debt payments, causing the default of 1575. That crisis became a harbinger of the decline of the Spanish Empire, despite the continued growth of captured territories.

1584: succession crisis in France. 1583: the Throckmorton Plot, backed by Spain to overthrow Elizabeth I and elevate Mary Queen of Scots to the English throne, which contributed to the deterioration of relations between the Spanish Empire and the rising British Empire. 1585: Philip II imposed an embargo on all English goods in Spain.

1587: crisis in England’s export trade and the execution of Mary Queen of Scots, who had been intended for the English throne. This set the stage for the Spanish Armada’s attack on Britain, which ultimately took place in 1588.

1594: the start of the Franco-Spanish War.

1602 (1603): the death of Queen Elizabeth I, which led to the accession of James VI of Scotland (James I of England). This created tensions between the crown and parliament, one of the precursors to the English Civil War. The Great Famine in European states was also occurring at this time.

1621: a new phase of the war between Spain and the Dutch, a continuation of the Eighty Years’ War. This worsened the situation in the Thirty Years’ War, which had begun in 1618, intensifying the general conflict between Catholic and Protestant powers and spreading tensions across Europe.

1629: Holy Roman Emperor Ferdinand II issued the Edict of Restitution, which aggravated religious and political conflicts within the empire and led to a further escalation of the Thirty Years’ War. A plague outbreak also occurred in Italy.

1638: Tulip Mania, which had begun in 1637. The first documented financial bubble on the Amsterdam exchange.

1641: parliamentary crisis, the English Monarchy, and civil war.

1648: the Peace of Westphalia, which ended the Thirty Years’ War and the Eighty Years’ War in Europe, bringing a shift in world order. The Holy Roman Empire lost influence. This also created economic problems for Spain, along with a transition into a period of debt crises and decline.

1656 (1657): plague epidemic in the Kingdom of Naples. Default on debts in Spain, after which the French began winning their wars.

1665: plague epidemic in London, the second wave of plague, which severely disrupted the economies of England and Europe as a whole.

1675: King Philip’s War in New England. A conflict between Native Americans and settlers in New England.

1683: the Liberation of Vienna from the Ottomans at the Battle of Vienna. This event ended the Ottoman threat to Europe, significantly altering Europe’s political and economic landscape and increasing the influence of the Austrian Empire.

1692: the year before the Battle of Marsaglia in 1693 (against Austria) and Neerwinden (against England, the Dutch Republic, and Spain), where France won but exhausted all its resources.

1695: prolonged famine in Scotland. Poor harvests in 1695, 1696, 1698, and 1699, combined with the economic downturn caused by the Nine Years’ War, led to severe famine and population decline. This tragedy coincided with the Great Famine in Estonia, Finland, Latvia, Norway, and Sweden.

1702: War of the Spanish Succession.

1711 (1710): France, under the rule of Louis XIV, was experiencing economic decline, partly due to the costs of wars. Crisis in Anglo-Scottish relations. Agricultural losses in Great Britain as a result of the Great Frost of 1709.

1719: the Mississippi Company stock bubble in France. It became one of the most famous financial bubbles of the 18th century, leading to a large-scale financial collapse. It coincided with the South Sea Bubble in Great Britain. In 1720, a plague outbreak occurred in France.

1729: famine, currency devaluation, and growing tensions between Catholics and Protestants in Ireland, aggravated by severe trade restrictions imposed by England, leading to further poverty and mass emigration.

1737: an inaccuracy, no significant upheavals occurred. Only in 1738 was there a harvest crisis in Scotland.

1746: the Battle of Culloden, which had long-term consequences for Scotland and Great Britain. During this same period, the War of the Austrian Succession continued in Europe, shaping the political and economic situation across the continent.

1756: the Seven Years’ War, which engulfed much of Europe, as well as colonies in America and Asia. In essence, the first global war.

1764 (1765): the Sugar Act, aimed at tightening control over the trade in sugar and other goods, which created tensions between the colonies and the metropole. The end of the Seven Years’ War in 1763 also increased the debt burden of the participating states.

1773: crisis in Great Britain following the Bengal Famine. The Boston Tea Party, a protest by American colonists against the tea tax imposed by the British Empire. This event became the catalyst for the beginning of the American struggle for independence. The collapse of the East India Company.

1783: the American Revolution. Great Britain lost control over its colonies in North America, reducing its revenues.

1791: the year before the Panic of 1792, the first major financial crisis in the young American republic, and the War of the First Coalition.

1800 and 1803: recession in the U.S., which followed the Peace of Amiens, ending the war between Great Britain and France. Commodity prices fell sharply. Trade was disrupted by pirates, which led to the First Barbary War. Soon after, Great Britain and France entered a new phase of the prolonged Napoleonic Wars.

1810: a sharp economic crisis in Great Britain, caused by overproduction and speculation linked to trade with America and the colonies. Two years later, in 1812, a brief recession in the U.S. preceded war with Great Britain.

1819 (1818): a panic that became the first major depression in U.S. history. It was caused by excessive credit expansion and land speculation, which led to the collapse of many banks and businesses.

1827: the year before the recession of 1828-1829, which was a precursor to the great depression of the 1830s.

1837: panic, financial crisis in the U.S.

1845: the Mexican-American War, the Great Irish Famine, a subsistence crisis in Europe, a late recession in the U.S.

1854: the Crimean War. Recession in the U.S. in 1854-1855.

1857: panic in the U.S. following the collapse of Ohio Life Insurance and Trust Company.

1864: the period of the American Civil War, collapse of the Indian stock market. The year before the recession of 1865-1867 in the U.S.

1873 (1872): panic that began in the U.S. and spread to Europe, a result of speculation in the railroad sector and a sharp contraction of the credit market.

1881: the end of major Native American resistance to U.S. authority. Britain’s defeat in the Anglo-Boer War, leading to the founding of the South African Republic. The year before the depression of 1882-1885 in the U.S.

1891: panic in the U.S. occurred in 1890, 1893, and 1896.

1899: recession in 1899-1900, connected to the start of the Second Anglo-Boer War. Two years later, the Panic of 1901 and another recession.

1908: period of recession in the U.S. (began in 1907).

1911: Antitrust crisis, Panic of 1910-1911.

1918: the Spanish flu and economic hardship following the end of World War I.

As the data was traced back into the distant past, the gradual transformation of global economic processes became evident. Where modern crises have a universal scale, earlier historical events were more localized, affecting individual empires and civilizations without exerting a total impact on the world economy, only on specific geopolitical regions.

A few charts reflecting the state of economies in the past: a wholesale price index for commodities and the level of international investments as a percentage of developed world GDP.

The peak of the wholesale price index aligned quite accurately with Benner’s high-price years, A and B. Discrepancies appeared only in 1819, 1845, and 1899, but each of those periods was defined by wars or panics.

The same pattern holds for international investment levels in the 19th century. The one exception is 1864, which negatively correlated with wholesale prices, particularly in the U.S. That price decline was connected to the Confederate currency reform of 1864, carried out to shrink the money supply during wartime. At the same time, the industrial revolution was generating significant investment flows.

Recent Past

1926 (1927): recession in the U.S.

1935: a two-year miss. The recession in the U.S. began in 1937.

1945: the aftermath of World War II, recession.

1953: recession in the U.S., caused by reduced spending after the Korean War.

1962: the Cuban Missile Crisis.

1965: the beginning of large-scale U.S. military operations in Vietnam and increased war spending.

1972: collapse of the Bretton Woods system (December 18, 1971). Then 1973: the energy crisis.

1980 (1981): recession in the U.S.

1989: the end of the Cold War, the year before the U.S. recession. Collapse of the Japanese stock market following the peak of the speculative real estate and equity bubble.

1999: the year before the dot-com bubble.

2007: the year before the mortgage crisis.

2016: Brexit.

2019: the COVID-19 epidemic.

Recent events draw the most attention, as the current generation is familiar only with the crises of 1999, 2008, and 2019, each of which was identified by Benner back in 1875. All of these events have led us to the current troubled state of the global economy.

The Future

Benner’s Cycles can also be extrapolated into future years to identify potential crisis periods.

The nearest peak is projected for 2026, after which a crisis becomes a possibility. The list of potential risks for such a scenario is extensive.

An escalation of trade wars between major economies and the spread of protectionist tendencies, which would disrupt global supply chains, will affect the volumes of international trade and investment. Existing geopolitical conflicts, for example in the Middle East or Ukraine, could play an equally destabilizing role if they intensify, as could the emergence of new flashpoints.

Looking further out, the rapid spread of automation and artificial intelligence also poses a risk, as it could trigger mass layoffs, particularly in knowledge-work sectors. Alongside this, the heavy debt loads of governments need to be taken into account, as do demographic shifts: the aging of the baby boomer generation and falling birth rates. More retirees and fewer young working-age people in developed countries means a deeper government debt hole. These factors carry additional complications for healthcare and social welfare systems, whose financial positions are already in question.

The threat of external shocks linked to natural disasters, climate change, and epidemics remains, capable of destroying supply chains. And the financial bubbles that formed on the back of the AI boom and the COVID-era money printing could burst with equally dramatic consequences as they have before in stock market history. This is only the visible portion of the risks.

A combination of several of the factors listed above creates all the conditions for an economic downturn in the coming years. And even a freeze on military conflicts would be unlikely to positively affect the outcome.

The yield curve (the yield spread between 10-year and 2-year bonds) has historically often foreshadowed economic downturns. But the current situation is further complicated by the critical growth of U.S. government debt. Since 2020 it has increased by $13 trillion and reached $36 trillion (43% growth in four years). By 2034, it is projected to reach $57 trillion, though this forecast does not even include the probability of a recession.

The debt-to-GDP ratio has exceeded 121%, higher than the levels seen during World War II. The U.S. spends 17.9% of all federal revenues on debt service alone (the highest figure since 1993), due to high Fed interest rates.

Rate cuts would be a beneficial step, as they would reduce the yield on new bonds, lowering budget expenditures on debt payments. The government would be able to start buying back existing high-yield bonds, replacing them with new bonds at lower interest rates, which would allow current debt to be refinanced on better terms.

With further rate cuts, the gap between short-term and long-term bonds will begin to narrow. 2-year and 10-year bonds will gradually return to their normal configuration, where long-term yields exceed short-term yields. The paradox is that these actions typically coincide with the start or peak of a recession: they don’t prevent it, they help soften the consequences and accelerate economic recovery. The closed loop of Fed policy will lead to further weakening of the dollar and the American economy, which will aggravate political and economic instability going forward.

The more distant outlook is no less grim, even looking at the forecast for the world’s largest economies through 2075.

According to Goldman Sachs estimates, China will overtake the U.S. around 2035, while India should catch up to China by 2075. The potential growth rate in the U.S. is significantly lower than in major developing economies, including China and, especially, India, where demographic growth plays an enormous role.

Shifts of this magnitude in the world order have always been accompanied by major wars. As far back as 2016, the Atlantic Council identified several plausible military conflicts that could occur (some are already in progress).

West and Russia. Tensions between Russia and the West, particularly in the context of Ukraine, will lead to military conflicts in the post-Soviet space and Eastern Europe.

East Asia. China is actively building up its naval and conventional forces, substantially altering the regional balance of power. The military expansion affects the strategic interests of U.S. allies in the region, including Japan, Taiwan, Vietnam, Malaysia, the Philippines, and South Korea. Beijing’s growing ambitions call into question the U.S. ability to guarantee the security of its partners and create a new geopolitical reality with potential conflicts.

South Asia. The conflict between India and Pakistan carries the greatest risk of going nuclear. The Kashmir problem remains the single biggest obstacle in bilateral relations between these two countries. Pakistan, lacking a clearly formulated nuclear doctrine, follows a first-strike principle (unlike India, which has declared it will not be the first to use nuclear weapons). Given Pakistan’s posture, any direct confrontation with India could trigger a nuclear war.

Middle East. Continuing instability in Syria, Iraq, Iran, Libya, and Yemen could lead to further spread of conflicts, including clashes between Sunnis and Shias.

Central and East Africa. A growing number of conflicts between Muslim and Christian populations could lead to violence in countries such as Sudan and Nigeria.

But all wars are fought over specific material assets: territory, natural resources, transportation corridors, and economic holdings. When economic disputes between countries reach a critical point and diplomacy stops working, the military mechanism for redistributing property and zones of influence kicks in. Every war is a pragmatic calculation, where the winner is whoever controls more valuable economic assets and has greater military capacity to seize them. The probability is high that the coming cycles will bring new wars and changes in world order.

Cyclicality Is Not a Strategy

At first glance, the logic seems simple: following Benner’s variations, buying during economic downturns (C) and selling during upturns (A and B) appears to be a winning strategy. But after examining a significant body of evidence, it becomes clear that this investment approach guarantees not profit, but at best the preservation of capital.

Financial analyst Vasco Laranjo conducted his own analysis of three strategies based on Benner’s Cycles.

Strategy 1. Buy during bad economic conditions (C) and sell during good conditions (B).

Strategy 2. Buy during bad conditions (C) and sell during panic years (A) and good conditions (B).

Strategy 3. Similar to Strategy 2, but adds another type of trade: short positions between periods A and C.

The research findings showed that among the three strategies tested, Strategy 2 demonstrated the most attractive return profile, managing to avoid losses. But its returns are significantly lower than the classic buy-and-hold strategy. This is confirmed by an independent backtest by Charles Edwards, conducted on Dow Jones Index data.

Despite the absence of clear losses, the practical application of cycles as a trading strategy comes with limitations. Using fixed dates for entering and exiting positions inevitably reduces potential returns, since real economic processes rarely align with the start of a year on January 1st. Low-price periods (C) are also characterized by significant uncertainty: a crisis can often be shorter than forecast, which makes buying exclusively during C periods questionable.

But the far more interesting topic for observation is periods A and B. When moving away from strict attachment to specific dates and toward analyzing peak prices in the corresponding years using the S&P 500 Index, Benner’s Cycles demonstrate fairly high accuracy.

At first glance, 1927 might seem like a poor year, but adjusted for inflation, locking in profits during that period could have served as insurance for investors against the catastrophic losses of the Great Depression in the early 1930s. At the same time, the 2016 Brexit events, despite the scale of the political development, did not produce significant market upheaval.

Benner’s Cycles essentially function as an indicator of elevated investment risk and clearly illustrate critical periods when purchasing financial assets becomes an extremely risky endeavor. A telling example: 1999. The popular buy-and-hold strategy in that case would have meant waiting approximately 15 years to return to breakeven.

But to truly appreciate Benner’s Cycles, one needs to go beyond the time frame of modern financial indexes. So let’s look at the reconstructed Dow Jones Index from 1789.

Peaks during periods A and B also align with many index peaks in line with economic events of the time, including the exception of 1803, the U.S. recession.

Findings

All of this work was done to assess the accuracy of Benner’s method. In most cases, years A and B do indeed signal a high probability of panic and a strong likelihood of crisis events.

Dow Jones Index: Alignment of Dow Jones peak with period A/B: 79.3%. Time period: 1803-2019.

S&P 500 Index: Alignment of S&P 500 peak with period A/B: 71.4%. Time period: 1873-2019.

Crisis events: Alignment of crisis events with periods A/B: 94.4%. Time period: 1575-2019.

I would interpret Benner’s Cycles less as a trading tool and more as an additional indicator of elevated risk, during which purchasing risky financial assets can lead to losses, while selling can reduce potential downside.

Conclusion

Samuel Benner was a pioneer in the study of market cycles. His work, which began as a personal search for answers after going bankrupt, resulted in one of the first models of market cyclicality. Today his ideas are not applied in mainstream financial market analysis, but the statistics show that cyclicality has not gone anywhere.

Although times have changed and the economy has grown more complex, the fundamental principles of periodicity that Benner laid out remain relevant.

Energy is an advantage

Energy resources are the foundation of any economy, and their role extends far beyond everyday needs like lighting or heating. Access to energy determines a country’s power and competitiveness on the world stage.

Energy powers factories and enterprises, enabling the production of goods on which the economy depends. Metallurgy, chemical manufacturing, technology: all of these industries require enormous energy inputs. High energy prices raise production and logistics costs, making products less competitive on international markets. This matters particularly for countries that depend entirely on energy imports. Without a stable and affordable energy supply, production stops, GDP falls, jobs disappear, and the economy weakens.

In geopolitics, energy is leverage. Countries with large reserves of oil, gas, coal, or uranium can dictate terms, influence prices on global markets, and shape relations between states. Control over energy resources tends to become a decisive factor in international conflicts and alliances alike.

The transition to cleaner energy is an important factor that deserves its own article. Go deeper and it’s not simply a hyped environmental goal; it’s a necessity for maintaining the economic resilience of many states in conditions of global change. Countries that invest in renewable energy and energy-efficient technologies gain a strategic advantage, reducing dependence on oil and gas price volatility and minimizing geopolitical and climate-related risks. Apply basic logic: even building and manufacturing the new technologies that will accelerate the transition to “green” energy requires, again, energy. For that reason, the transition path will likely take several decades.

So if any energy source capable of increasing productivity and generating profit matters, what energy resources does Ukraine actually have?

A graveyard of resources

Ukrainian territory holds strategic importance in the global economy because of its resources. It contains significant reserves of oil, natural gas, and coal, which form the foundation of Ukraine’s energy potential. It also holds minerals critical to modern industry, including rare-earth metals in high demand on global markets.

Ukraine ranks among the world leaders in assessed mineral wealth. The country is home to 117 of the 120 most widely used minerals, holds the largest assessed uranium reserves in Europe, the second-largest reserves of iron ore, titanium, and manganese, and the third-largest reserves of shale gas.

The eastern and southern regions of Ukraine are key agricultural zones, as noted by the USDA. Ukrainian wheat, corn, barley, and sunflower supply food to many countries around the world. The war has not only disrupted the extraction of these resources but also blocked export routes, threatening global food supply chains. Supply route blockades imposed by several European countries have compounded the problem.

Setting aside agriculture and coal, many of Ukraine’s resources remained underdeveloped and unstudied throughout the Soviet period and much of the post-Soviet era, due to a lack of technology and investment. This continues to play an enormous role today, keeping the “agrarian superstate” label in place. Most of the population still believes the country’s exclusive wealth lies in the fertility of its land, but that is only a portion of the resources in use and in plain sight. Let’s go through them in order.

Gas relations

Natural gas and the pipelines carrying it bound Russia and Ukraine within the Soviet space without any friction (logical under a communist regime). But after Ukraine’s independence, Ukraine became, in part, a competitor to Russia. Gas imported from Russia was Ukraine’s primary energy source and played a decisive role in powering its economy and industry for an extended period. The transit fees Ukraine collected for transporting Russian gas to Europe through its pipelines and territory were an important source of budget revenue.

Access to Europe via the Ukrainian pipeline system was critical for Gazprom, since the European market was its main revenue source. This meant Russia depended on Ukraine. In 2005, 80% of Russia’s gas exports to Europe passed through Ukrainian pipes. Under the Soviet Union, the question of gas supplies never arose. Under Ukrainian independence, it became one of the central issues.

Political fault lines in Ukraine then followed, tied to two poles. Simply put: Yanukovych represented Kremlin interests; Yushchenko embodied a European political course, his wife having previously worked in the administration of U.S. President Reagan.

Yushchenko’s victory in Ukraine’s presidential election led to difficult negotiations over natural gas prices. Until that point, Ukraine had been paying Russia roughly one-third of what EU countries paid. There was no point in subsidizing Ukraine with cheap gas given that even with subsidies it had accumulated enormous debts for gas deliveries, and was now governed by a president seeking independence from Russia.

One of the Kremlin’s political objectives became control over the Ukrainian gas transport system, critical for moving Russian gas to Europe. For Ukraine, the same system was an equally important political and economic instrument.

Without reaching an agreement, on January 1, 2006, Gazprom cut off supplies destined for Ukraine. Ukraine then began drawing on gas intended for the eurozone, which reduced supplies to Europe and caused friction in relations between Russia and the EU. Western countries read this as a demonstration of energy power as a political tool.

After that episode, both sides reached a new agreement. Ukraine retained control over its pipeline system, but the company RosUkrEnergo continued to play a significant role in gas relations.

The same scenario played out on January 1, 2009, when Russia again cut off supplies destined for Ukraine, claiming Ukraine was stealing gas meant for Europe. Gas deliveries to Europe also stopped as a result. Ukraine and the eurozone managed the situation using stored reserves, after which a new agreement was reached. Cutting supplies doesn’t serve Russia’s interests either.

To diversify its supply routes around Ukraine, Russia built other pipelines: Yamal-Europe through Poland, and Blue Stream running from Russia under the Black Sea to Turkey. But Nord Stream, between Russia and Germany along the Baltic seabed, was far more significant. Germany is the largest economy in the EU, and a direct route lowers gas transport costs. It allowed Russia to sell gas to Germany without paying transit fees to intermediary countries like Ukraine or Poland, making the route more profitable.

The eurozone’s goal, meanwhile, was flexibility and diversity in energy supply, to avoid being locked into imposed prices and conditions. European companies began interconnecting pipelines to allow supply routing to shift as needed. Emphasis was also placed on building liquefied natural gas (LNG) terminals and creating larger gas storage facilities. This expanded Europe’s ability to receive gas not just by pipe but by sea, with the U.S. becoming the main supplier via that route.

European policy pushed to move away from old contract structures in which gas was sold on terms of up to 20 years or more, with prices tied to oil. The EU instead sought to introduce more competitive and transparent markets where buyers and sellers could interact freely. This meant a transition from long-term, fixed supplier-buyer relationships toward markets where conditions and prices could be adjusted flexibly in response to market conditions.

In 2013, Ukraine found itself at a crossroads between East and West, and on the surface it looked purely like a trade question. On one side, Russia was actively promoting the “Eurasian Economic Union,” a grouping of post-Soviet countries under its leadership with a common tariff regime and a unified economic space. At the same time, Ukraine was negotiating an association agreement with the EU that would entail deeper economic integration with Europe.

The problem: these two paths were completely incompatible. Ukraine could not be part of both systems simultaneously, since the tariff rules and economic conditions of the EU and the Eurasian Union were mutually exclusive. If Ukraine chose integration with the European Union, it would have to abandon participation in the Kremlin’s Eurasian project. Beyond that, through integration into Europe, Russia would have gained a new competitor on several fronts at once. The key one: gas.

Ukraine was trying to increase its economic and energy security by developing its own natural resources and diversifying away from Russian imports. In August 2012, the government selected ExxonMobil and Royal Dutch Shell to lead development of the Skifske deepwater natural gas field on the Black Sea shelf, alongside Romanian OMV Petron and the national joint-stock company Nadra Ukrainy.

In 2013, a large-scale oil and gas privatization began. That same year, at the World Economic Forum in Davos, Shell signed a deal with Ukraine to extract shale gas from the Yuzivska field (Donetsk and Kharkiv regions), one of Ukraine’s largest oil and gas deposits. Resource volumes at that site alone exceed 1 trillion cubic meters. Chevron subsequently signed a shale gas extraction deal at the Olesska field (Lviv, Ivano-Frankivsk, and Ternopil regions).

This “European course” was not in Russia’s interest, since Ukraine’s gas reserves in the Olesska field alone could have satisfied three years of EU demand (approximately 3 trillion cubic meters).

Kremlin representatives understood that signing the EU association agreement would exclude Ukraine from the Eurasian Economic Union. Yanukovych consequently walked away from the agreement, receiving a $15 billion “gift” loan from Russia in return. What followed was the clash of interests and the widely known events on the Maidan. All agreements with foreign energy companies on gas extraction were subsequently severed with the annexation of Crimea and the start of the war in the Donbas in 2014.

Work on Nord Stream 2 began in late 2015, four years after the first pipeline launched. But the second project faced resistance. Much of the opposition was directly connected to the situation in Ukraine. Criticism came from Poland and the Baltic states, as well as from the European Union itself: a sharp contrast to its earlier support for Nord Stream 1.

On one side sat concerns about increased dependence on Russian gas and potential geopolitical consequences. On the other, economic interests and the desire for stable energy supply also played a substantial role in decision-making. Some leaders, including German Chancellor Angela Merkel, treated Nord Stream 2 as a purely commercial project whose decisions should be made by the participating companies, not by politicians.

But the pipeline was not “just an economic project,” as Chancellor Merkel said. Political factors were also built in: ensuring the continuation of gas transit through Ukraine, to avoid depriving it of transit revenues and creating economic pressure on the country. Without that provision, the new pipeline would have allowed supplies via Ukraine to be reduced to minimal volumes, barely enough to keep the old routes technically operational. Germany, naturally, gained a key benefit from this arrangement: the ability to present itself as a partner to everyone.

In December 2019, construction of Nord Stream 2 was in its final stages. On December 20, Russia and Ukraine signed an agreement guaranteeing five years of natural gas supplies to Europe via Ukraine, securing a level of transit income for Ukraine. Russia also agreed to pay Ukraine $3 billion in settlement of an arbitration claim Ukraine had won against Gazprom. That amount was roughly equivalent to one year of transit revenue for Ukraine. The war was ongoing throughout this entire period, worth keeping in mind.

Today Europe is part of a global market with a wide range of gas supply options. As a result of the war, Ukraine is forced to import gas from Slovakia, Hungary, and Poland. It’s understood that part of what is imported is Russian gas mixed with LNG from other countries. All of this continues today. But 2025 is considered the deadline on supplies, around which debate is also ongoing.

Beyond that, as noted earlier, Ukraine holds significant natural gas production potential. Approximately two-thirds of the country’s total domestic gas demand is met by local production. Ukraine may hold the largest natural gas reserves of any European country, opening prospects for increased domestic output and exports in the future.

Most of current gas production in Ukraine (roughly 80%) is carried out by the state gas company. The second-largest producer is a private company called Burisma. It accounts for only 5% of domestic output but acquired outsized notoriety outside the energy sector. This is the company Donald Trump wanted investigated in Ukraine because Hunter Biden, son of then-Vice President Joe Biden, sat on its board of directors.

All of that led to Trump asking President Zelensky in a July 2019 phone call to investigate the Bidens’ activities in Ukraine. That call and the subsequent actions of the Trump administration became the subject of a congressional investigation, ultimately leading to Donald Trump’s first impeachment in December 2019. Complex, I understand, but all of these political actions only underscore the importance of Ukraine’s energy sector as a sphere of influence.

Oil

There isn’t much publicly available information about Ukrainian oil and its quantities. The International Energy Agency (IEA), for instance, notes that “Ukraine has only small oil reserves (exact volumes are a state secret), and legislation on emergency oil supplies that would regulate the use of strategic oil reserves in the event of supply disruptions does not exist.”

In the largest digital atlas (SOAR), I came across maps that also indicate oil deposits.

In the right portion of a map from the complete geological map of the former Soviet Union, the location of oil and gas fields is visible in the northeastern region of Ukraine, where approximately 2,000 km of border with Russia runs through the Dnieper-Donetsk geological basin: the primary oil province of Eastern Europe. Oil fields are marked with white dots, gas fields with blue.

At a minimum, it’s reasonable to accept the fact that oil exists on Ukrainian territory. The exact reserve quantities are almost certainly not findable in open sources.

Uranium

Nuclear energy is expanding rapidly worldwide, particularly in countries like China, India, and the United States. Despite these trends, natural uranium production has been declining in recent years, and the gap between production and consumption grows each year. That gap is approximately 20,000 tonnes of uranium annually, currently being filled by existing stockpiles. This situation could lead to a sharp spike in uranium prices in the near future, once stockpiles begin to run out: they are not unlimited. Below is a list of countries with the largest uranium deposits and their metrics on resource reserves, production capacity, consumption, and deficit.

According to the IAEA, Ukraine has been a member of that organization since its founding. But Ukraine’s uranium resources have not been fully explored or studied. All deposits are located in the Ukrainian Shield and the Dnieper-Donetsk Depression. The estimated uranium resource base: 185 kilotons, making Ukraine the largest uranium deposit in Europe and the 11th largest in the world.

Ukraine has a highly developed nuclear energy sector, generating more than 55% of total electricity output. At the same time, domestic uranium mining can meet only 30% of its needs. Energoatom purchases the remaining uranium from other countries. So, despite holding the largest deposits, the country lacks the technology and investment to extract uranium at a scale that would cover 100% of domestic needs, let alone export.

With energy consumption growing in developed economies and uranium deficits widening, uranium could become one of the key geopolitical objectives.

Critical raw materials

More recent research conducted by SecDev showed that the territories currently under Russian occupation contain hydrocarbons valued at more than $12 trillion, while the total estimated value of Ukraine’s national resources is assessed at over $26 trillion. These are precisely the figures cited by senior U.S. Senator Lindsey Graham in his Face the Nation interview:

Research by the Ukrainian Geological Survey indicates that ancient rock formations of the Ukrainian Shield conceal lithium deposits with enormous potential. These deposits are located primarily in the area around Mariupol, a port city in the Donbas. The New York Times published the same findings.

In 2021, the EU was already attempting to diversify away from China and Russia through a “strategic partnership” with Ukraine, since Europe is critically dependent on raw material imports from China, which accounts for 98% of supply.

The bulk of these materials is used in electric vehicle batteries, smartphones, and wind turbines. As of 2021, the EU’s critical raw materials list comprised 30 materials, including rare-earth elements, bauxite, lithium, and titanium. At least 20 of them are found in Ukraine.

China has become the global leader in the extraction and production of critical resources and has gained further geopolitical leverage as a result. This gives Beijing the ability to apply pressure on the economic and political decisions of other countries.

Protectionism is rising elsewhere as well: Zimbabwe and Chile are restricting lithium exports, while Indonesia and the Philippines are tightening control over nickel supplies. All of these moves are significantly disrupting global supply chains, making them less stable and predictable. With the full-scale war in Ukraine, prices for lithium and nickel, used in electric vehicle manufacturing and renewable energy development, rose sharply.

Platinum group metal prices also increased: these play a key role in the development of the hydrogen economy. The consequences of these processes are already being felt worldwide.

European industry, dependent on critical raw material imports, is beginning to deteriorate. Electric vehicles produced in the EU and the U.S. are gradually becoming uncompetitive compared to their Chinese counterparts due to the absence of alternative raw material supply routes. That is just one example of how dependence on a single supplier of critical resources can undermine the economic stability of entire countries and entire industries.

Conclusion

Ukraine has become the epicenter of geopolitical change, where the interests of autocratic regimes and democratic alliances have collided. Control over energy, food, and mineral resources is the primary objective, since each of these strategic resources is present in Ukraine in abundance.

Tracking all of these interconnections makes the logic of Russia’s and China’s actions more legible: both are working to strengthen their positions on the global stage in economic and geopolitical terms. Control over natural resources is the core strategic objective. This explains the “military competition” between autocratic and democratic states, in the Middle East or Africa for instance, where energy and rare-earth resources are also concentrated.

Historical, cultural, religious, linguistic, ethnic, and other factors, however significant, serve merely as tools for managing human resources. The true cause of conflicts is control over scarce natural resources. That is the key to global dominance.

The question arises: "Can the parties abandon strategic objectives in favor of temporary compromises?" The answer, I think, is obvious.

Subprime refers to “below standard.” More risky and speculative compared to standard, reliable credit.

Systemic risk is the risk of a breakdown across the entire financial system due to the scaling of problems at individual institutions or markets.

Securitization is the bundling of loans into standardized securities backed by those loans, which can then be traded like any other security.

QE is a non-standard central bank monetary policy in which the central bank (the Fed) purchases government and other securities on the secondary market, thereby increasing liquidity in the financial system.

LIBOR, or “London Interbank Offered Rate,” is the average interest rate on interbank loans in various currencies on the London market, at which banks lend each other U.S. dollars.

Treasury-bill rate is the yield on short-term U.S. government securities, Treasury bills (T-bills), issued by the U.S. Treasury with maturities of up to one year.

TED spread, or “Treasury-Eurodollar spread,” is the difference between the 3-month LIBOR rate and the 3-month Treasury-bill rate. This spread reflects the risk associated with interbank lending relative to essentially risk-free government bonds.

Case-Shiller Index is a measure of U.S. residential real estate price changes.

On the eve of the financial crisis

In early 2007, most people could not imagine that within two years the global financial system would face its most serious crisis since the Great Depression. At the time, the economy appeared to be growing. The last major disruption had been the collapse of technology companies in 2000-2002. The Federal Reserve had responded by cutting interest rates to prevent a recession.

First, the Fed’s rate cuts made credit more accessible and cheaper for both households and businesses. This boosted consumer demand and business investment. Lower borrowing costs meant that individuals and companies could take on more debt on more favorable terms.

Second, lower rates reduced borrowing costs for companies. This made it easier for businesses to access financing, supporting expansion. Cheaper credit helped companies innovate, scale up production, and create new jobs.

Third: housing market support. Lower mortgage rates made home loans more accessible and attractive to buyers. This stimulated new mortgage origination and drove demand for housing. The housing market stayed active on the back of lower rates.

Fourth, rate cuts contributed to stock market gains. Lower borrowing costs encouraged investors to take on debt to buy equities, increasing demand for shares and pushing prices higher. This created positive market sentiment and expanded investment.

Finally, lower rates reduced yields on dollar-denominated debt securities, which weakened the dollar and had a positive effect on U.S. exports and investment inflows. A cheaper dollar made American goods and services more competitive on world markets, supporting export growth.

As described above, the Fed’s rate cuts pulled down rates on other short-term debt instruments as well: LIBOR, Treasury bills, and the TED spread.

Chart 1 shows that from 2001 to 2004, Treasury-bill rates and LIBOR, the rate at which banks lend to each other, fell simultaneously. These actions helped prevent a prolonged economic downturn, and the recession was brief, without producing serious damage to the economy.

By the mid-2000s, the economy looked solid. After the sharp equity market decline in 2000-2002, markets began recovering sharply from 2003 (Chart 2) and recovered all losses from the technology crisis. The banking system also appeared sound.

One useful measure of bank stability was the TED spread: the difference between LIBOR (the rate at which banks borrowed from each other) and the Treasury-bill rate (the rate at which the U.S. government borrowed). In early 2007, this figure stood at just 0.25%, signaling that banks viewed default or financial system risk as unlikely.

The combination of low interest rates and a stable economy produced a real estate boom. U.S. home prices began rising in the late 1990s and accelerated after 2001, when rates fell sharply. Over the ten years from 1997, average property prices tripled (Chart 3).

But Americans’ confidence in low financial risk, the economy’s recovery from the tech crisis, and rising real estate prices supported by falling interest rates all contributed to the 2008 crisis.

On one side, lower rates reduced returns on various investments, and investors searched for higher-yielding alternatives. On the other, low volatility and confidence in the absence of risk made investors less cautious, leading to a rise in risky investments, including mortgage loans. The U.S. real estate and mortgage lending market ended up at the center of the crisis.

Risky practices in mortgage lending

Before the 1970s, most mortgage loans were made by local lenders such as savings banks or credit unions. Homeowners took out long-term loans, typically 30 years, to buy a house and repaid them gradually. The primary asset of these lending institutions was mortgage loans; depositor funds were the primary liability.

The situation changed in the 1970s, when Lewis Ranieri, a vice president at Solomon Brothers, introduced the idea of buying large volumes of mortgage loans from lenders, pooling them, and selling the pools as financial instruments. These pools became known as “mortgage-backed securities.” Fannie Mae and Freddie Mac took on this role. The process eventually came to be called securitization. Fannie and Freddie became dominant players in the mortgage market, purchasing more than half of all mortgages originated by private companies.

Diagram 1. Cash flows in mortgage-backed securities.

Diagram 1 shows the path of cash flows from the original borrower to the end investor in a mortgage-backed security. It starts with the originating institution (say, a bank) making a loan to a borrower for $100,000. The borrower repays principal and interest over 30 years. The lender then sells the loan to a company such as Freddie Mac or Fannie Mae and gets its money back.

The lender can continue to service the loan, collecting monthly payments from the borrower and earning a fee for doing so. The loan payments, net of that fee, are passed on to the agency (Freddie or Fannie). These agencies pool multiple loans into mortgage-backed securities and sell them to investors such as pension funds or investment funds. The agency (Fannie or Freddie) typically guarantees that credit risk within each pool is minimal and receives a fee for this. The remaining cash flows then pass to the end investor. Mortgage-backed securities are sometimes called “pass-throughs” because of this cash-flow structure.

Until the final decade before the crisis, most mortgages bundled into mortgage-backed securities were owned or guaranteed by the government-sponsored entities Freddie Mac and Fannie Mae. These loans were low-risk and met defined standards: the loan amount, for instance, could not exceed 80% of the home’s value.

As securitization developed, mortgages began appearing that didn’t meet these standards and carried higher default risk. Private companies began securitizing these “non-conforming” loans. The key distinction was that investors in these securitized loans had no government guarantee and therefore bore borrower default risk directly.

This created a situation in which mortgage brokers originating these loans didn’t always conduct proper borrower vetting, since they could sell the loans to investors without bearing responsibility for possible defaults.

Those investors, naturally, had no contact with borrowers and couldn’t conduct detailed credit quality checks. Instead, they relied on borrower credit scores, which gradually displaced traditional due diligence.

A consistent trend soon emerged toward loans with minimal borrower documentation, and then with no documentation at all, requiring no verification of the borrower’s ability to service the debt. Other underwriting standards deteriorated as well. The permissible leverage on residential loans (measured by the loan-to-value ratio) rose sharply. By 2006, most subprime borrowers were purchasing homes by borrowing the full purchase price.

When home prices began falling, these highly leveraged loans quickly went “underwater”: the house was worth less than the outstanding loan balance, and many homeowners chose to walk away from both their homes and their debts.

Adjustable-rate mortgages became very popular, particularly among borrowers with weak credit histories. These loans offered low initial rates that could later reset: rising to the prevailing market rate plus a spread. Many borrowers used these low initial rates to buy homes, but when the loan rate began to rise, their monthly payments increased sharply, particularly as overall market rates moved higher.

Despite the risks inherent in adjustable-rate mortgages, many investors were confident that continuous home price appreciation would bail out bad loans. But from 2004 onward, the ability to refinance began to diminish:

1. Rising interest rates began creating problems for homeowners with variable-rate mortgages.

2. Home prices peaked around 2006, limiting homeowners’ ability to use home equity for refinancing. In 2007, mortgage delinquencies and losses on mortgage-backed securities began rising sharply.

That was the beginning of a financial crisis that would eventually damage the entire world.

Why did investors buy risky mortgages?

In practice, these mortgage loans were not treated as risky, and their ratings were high. The answer lies in financial engineering: securitization, restructuring, and credit enhancement. These mechanisms allowed investment banks to create high-rated securities (AAA, for example) from junk loans. One of the most important and destructive instruments in this process was the collateralized debt obligation, or CDO.

CDOs were designed to concentrate default risk from a common loan pool on certain investors, leaving others better protected from that risk. The idea: take a large number of loans that people took out at banks, bundle them into a single pool, then split the pool into two parts called “tranches.” There are senior and junior tranches. Senior tranches receive payments first; they have priority and are genuinely secured (most likely to be paid). Junior tranches receive payments only after senior tranches have been paid.

Why does this matter? Because senior tranches were considered safer. Even with risky underlying loans, a default rate above 30% seemed unlikely, so senior tranches often received the highest rating (AAA) from major rating agencies: Moody’s, Standard & Poor’s, and Fitch. A pool of low-rated mortgage loans thus produced AAA-rated securities.

CDOs, in theory, helped manage risk and create safe investments even from some risky loans. With hindsight, it’s obvious those ratings were completely wrong. The CDO structure made senior tranches less protected than investors believed. When home prices began falling nationwide, defaults occurred everywhere, and the expected diversification benefits never materialized.

Why did rating agencies underestimate the risks in these securities?

First, they relied on historical data that failed to account for changes such as the sharp run-up in residential real estate prices and the shift in borrower types: people taking loans with no down payments, rising payment schedules, and minimal documentation.

Second, there were obvious agency problems. Rating agencies were simply paid to assign ratings. They faced pressure from issuers who could shop for the most favorable terms, with the goal of securing high ratings.

Credit default swaps

Parallel to the CDO market, the credit default swap market was developing during this period.

A credit default swap, or CDS, is essentially an insurance contract against the default of one or more borrowers. The buyer of the swap pays an annual premium (similar to an insurance premium) for protection against credit risk. CDS became an alternative method of credit enhancement, ostensibly allowing investors to buy subprime loans and insure their safety.

The problem was that some companies selling CDS did not have sufficient capital to cover their obligations. AIG, for instance, sold CDS on risky mortgage loans totaling over $400 billion, with no ability to pay out if defaults occurred.

The rise of systemic risk

By 2007, problems had accumulated in the financial system. Many banks and investment companies were using short-term borrowing to fund long-term investments (such as mortgage-backed securities). This meant they constantly needed to raise new financing to refinance old debts. They had also taken on very large debt loads and depended on a continuous inflow of new funds. If markets suddenly stopped providing new debt, default risk would materialize. Many of the assets purchased with borrowed money (mortgage-backed securities) were illiquid: they couldn’t be sold quickly to repay debts.

Investment firms had also dramatically increased their leverage, making them vulnerable to funding risk, especially if asset prices fell. Even small losses could render them insolvent, at which point no one would want to lend them new funds.

An additional source of problems was investors’ heavy use of credit protection products including CDOs. Many assets inside these pools were illiquid and difficult to value, with prices highly sensitive to forecasts about future credit markets. If the economy turned down and credit ratings fell, selling these assets would be a difficult task.

On regulated exchanges, such as futures and options markets, participants must post collateral to guarantee their obligations. Prices are marked to market daily, and gains or losses are automatically debited or credited to traders’ accounts. If losses are too large and collateral is exhausted, the trader must post additional funds or close positions to avoid insolvency. All participants can see each other’s positions and risks.

On “over-the-counter” markets, where CDS contracts traded, contracts were concluded between two parties with less information available on positions and risks. Tracking profits and losses over time and assessing each participant’s credit risk was far more difficult.

This financial model carried systemic risk: the potential collapse of the financial system when problems in one market spill into others. When lenders such as banks have limited capital and fear further losses, they may rationally choose to hold onto that capital rather than deploy it to clients such as small businesses, thereby compounding funding problems for their ordinary borrowers. Any reduction in lending always harms the broader economy.

The crash

By autumn 2007, home prices had fallen, mortgage delinquencies had risen, and the stock market had begun its freefall. Many investment banks with large exposures to mortgage loans also began to experience difficulties.

In September 2008, the crisis reached a catastrophic inflection point. First, the federal agencies Fannie Mae and Freddie Mac, which held enormous quantities of impaired mortgage loans, were placed under government conservatorship. This triggered panic on financial markets.

Two of the largest investment banks, which had suffered serious losses from purchasing risky assets, came under severe pressure: Lehman Brothers and Merrill Lynch.

Merrill Lynch avoided bankruptcy: Bank of America agreed to acquire the firm for $50 billion in a government-supported transaction. Merrill Lynch became a division of Bank of America. Merrill Lynch’s depositors and clients did not lose their funds; their accounts, assets, and liabilities were transferred to Bank of America (and it was an excellent acquisition for the buyer). Losses fell only on the company’s shareholders.

The sale of Merrill Lynch preserved the firm and prevented far larger shocks to the financial system that would inevitably have followed its bankruptcy. Lehman Brothers, unfortunately, found no buyer. No one was interested in acquiring the bank, as its losses were considerably larger than Merrill Lynch’s. Buying a bank means taking on all of its liabilities and problems, and neither other banks nor the government had an interest in facilitating such a deal. Because beyond Lehman, there was also the problem of AIG: the government was forced to extend a credit line to the company to prevent its collapse, since AIG had guaranteed enormous volumes of CDS transactions and its failure could have severely damaged the banking system. The Treasury then proposed a $700 billion facility to purchase distressed mortgage-backed securities.

The Lehman Brothers bankruptcy had a severe impact on the short-term credit market. Lehman had been borrowing large sums by issuing short-term debt instruments known as commercial paper. The primary investors in these instruments were money market funds, which invested in short-term, safe debt obligations. When Lehman failed, concerns arose that these funds might suffer losses on their large commercial paper holdings, and fund clients began withdrawing their money. The funds in turn began shedding commercial paper and moving into safer, more liquid Treasury bills. This caused short-term funding markets to close.

The freeze in short-term credit markets compounded the financial crisis. Large companies and banks found it difficult to obtain short-term credit. Small businesses that depended on bank loans were also affected and could not continue operating. Companies without sufficient capital were forced to scale back operations.

Unemployment rose sharply, and the economy entered a deep recession. The crisis on financial markets spilled into the real economy, and the damage spread across the world.

Wall Street reform

The crisis generated extensive discussion about how to improve financial system governance and reduce risk. In 2010, the Dodd-Frank Wall Street Reform and Consumer Protection Act was passed, proposing changes to reduce the danger of a systemic crisis.

The law requires banks to hold more capital: a financial reserve they must maintain. This matters because, if a bank runs into trouble, more capital allows it to better absorb losses and reduces the risk that its failure will affect other banks. Also, when banks have more of their own skin in the game, it may encourage them to take less risk, since they themselves bear the losses rather than government entities such as the FDIC.

Transparency requirements, particularly in derivatives markets. Standardizing CDS contracts, for instance, so they can trade on centralized exchanges for proper regulation. Daily margin requirements prevent CDS market participants from taking positions that exceed their capacity, and exchange trading makes it easier to analyze firms’ exposure to losses in these markets.

Restrictions on risky activities banks can engage in. The so-called Volcker Rule, named after former Fed Chair Paul Volcker, limits banks’ ability to trade for their own account and to own or invest in hedge funds or private equity funds.

Addressing regulatory oversight gaps that became apparent in 2008, by providing clear authority and accountability to different regulators to avoid duplication and “regulatory arbitrage,” where an issuer seeks out the most permissive regulator to boost securities’ credit ratings.

Compensation structures that incentivize employees to act in the company’s long-term interest rather than for quick profit. Specifically, employee compensation (bonuses, options, etc.) should depend on long-term performance results, not just quarterly or annual profit figures. If executives misstate financial results to show better numbers and collect bonuses, they should be required to return those bonuses. This is meant to prevent excessive risk-taking by companies and their executives: banks sometimes take enormous risks in pursuit of large profits and corresponding bonuses for senior management, but when things go wrong, losses fall on the company and even on the government. Rating agency incentives: few people are comfortable with a system in which rating agencies are paid by the firms they rate. The law provides for the creation of an Office of Credit Ratings within the Securities and Exchange Commission to oversee rating agencies.

All of this is logical and coherent. But was the law fully implemented?

No. Some provisions of Dodd-Frank were enacted, but key points never took effect or were later rolled back. The Volcker Rule was substantially weakened and allows banks to invest in hedge funds and private funds within defined limits. Requirements for the segregation of client funds and banks’ proprietary operations were never adopted. Derivatives trading regulation was relaxed in favor of banks. Proposals to cap executive compensation at banks found little support. And reform of rating agencies produced no meaningful change to their business model or conflict of interest. The crisis did not, in the end, lead to the sweeping and successful reforms that had been intended.

The printing press and the Fed

Wall Street reforms were unsuccessful, but the shift in Fed policy is a different story. In response to the global financial crisis of 2008, the Fed fundamentally changed the direction of its policy. These changes were a response to the scale of the disruption to the economy and financial system, and reflected central banks’ transition to quantitative easing.

After the Lehman Brothers bankruptcy in September 2008, the global economy faced a sharp contraction in liquidity, collapsing equity markets, and the risk of a full-scale crisis. The Fed under Ben Bernanke began aggressively cutting interest rates. By December 2008, the rate had been lowered to essentially zero (0-0.25%), where it remained for seven years, until December 2015.

This aggressive monetary stimulus was meant to support consumer demand, prevent deflation, and stabilize the financial system. But rate cuts alone, as a regulatory tool, proved insufficient. Non-standard measures were needed to flood the economy with liquidity.

In November 2008, the Fed announced the first round of its quantitative easing program (QE1). Under QE1, the Fed purchased mortgage-backed securities and government bonds totaling up to $1.75 trillion: a “printing press” to expand the money supply and stimulate demand. QE1 ended in March 2010, but a second round (QE2) launched in November 2010, purchasing $600 billion in assets through June 2011. QE3 started in September 2012, with the Fed buying $85 billion in assets per month. Over the full QE period from 2008 to 2014, the Fed expanded its balance sheet more than fourfold, from $900 billion to $4.5 trillion.

Large-scale quantitative easing was accompanied by a shift to a 2% annual inflation target. This allowed stimulus policy to remain in place longer under conditions of low inflation. Another development was the introduction of forward guidance in 2011: the Fed began publishing projections of the future path of interest rates, substantially improving the transparency of monetary policy for markets.

The Fed’s QE measures from 2008 to 2014 succeeded in avoiding a deflationary spiral, stabilizing the banking system and equity markets, and maintaining credit availability. The one problem: the new Fed measures did not solve the problem of low economic growth and contributed in large part to the formation of financial bubbles through excess liquidity. The impact of Fed policy in the post-2008 period remains a subject of debate to this day.

Conclusion

Ben Bernanke, Fed Chair from 2006 to 2014, cited in one interview the emergence of panic and the loss of confidence in the financial system as the primary cause of the global financial crisis of 2008: a modern-day “bank run”:

The human factor, specifically the fear and panic of depositors, played a central role in the escalation of the financial crisis. Despite the existing economic problems and structural deficiencies, there is no country that has ever been immune to financial imbalances and disruptions. As an economy grows, the number of potential risks always grows with it.

In fact, in 2008 the share of low-credit-quality subprime loans in the overall mortgage market was not large. The majority of mortgage loans had been made to borrowers with solid credit histories and collateral. The question of ensuring banking system liquidity could also have been resolved without the need for the Fed's aggressive quantitative easing. But depositor panic, and regulators' insufficiently rapid response, created additional difficulties and made the situation worse.